Package 'BART' reference manual

Title:	Bayesian Additive Regression Trees
Description:	Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.
Authors:	Robert McCulloch [aut], Rodney Sparapani [aut, cre], Robert Gramacy [ctb], Matthew Pratola [ctb], Charles Spanbauer [ctb], Martyn Plummer [ctb], Nicky Best [ctb], Kate Cowles [ctb], Karen Vines [ctb]
Maintainer:	Rodney Sparapani <[email protected]>
License:	GPL (>= 2)
Version:	2.9.9
Built:	2024-12-21 06:57:14 UTC
Source:	CRAN

Bayesian Additive Regression Trees

Description

To avoid duplication, the main references that this package relies upon appear here only. For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.

References

Sparapani R., Spanbauer C. and McCulloch R. (2021) Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package. JSS, 97, 1-66. <doi:10.18637/jss.v097.i01>.

Chipman H., George E. and McCulloch R. (1998) Bayesian CART Model Search. JASA, 93, 935-948. <doi:10.1080/01621459.1998.10473750>.

Chipman H., George E., and McCulloch R. (2010) Bayesian Additive Regression Trees. Annals of Applied Statistics, 4, 266-298. <doi:10.1214/09-AOAS285>.

Sparapani R., Logan B., McCulloch R. and Laud P. (2016) Nonparametric Survival Analysis Using Bayesian Additive Regression Trees (BART). Statistics in Medicine, 35, 2741-2753. <doi:10.1002/sim.6893>.

Sparapani R., Logan B., McCulloch R. and Laud P. (2020) Nonparametric Competing Risks Analysis Using Bayesian Additive Regression Trees (BART). SMMR, 29, 57-77. <doi:10.1177/0962280218822140>.

Sparapani R., Rein L., Tarima S., Jackson T. and Meurer J. (2020) Non-Parametric Recurrent Events Analysis with BART and an Application to the Hospital Admissions of Patients with Diabetes. Biostatistics, 21, 69-85. <doi:10.1093/biostatistics/kxy032>.

Gramacy R. and Polson N. (2012) Simulation-based regularized logistic regression. Bayesian Analysis, 7, 567-590. <doi:10.1214/12-ba719>.

Albert J. and Chib S. (1993) Bayesian Analysis of Binary and Polychotomous Response Data. JASA, 88, 669-679. <doi:10.1080/01621459.1993.10476321>.

De Waal T., Pannekoek J. and Scholtus S. (2011) Handbook of statistical data editing and imputation. John Wiley & Sons, Hoboken, NJ.

Friedman J. (1991) Multivariate adaptive regression splines. Annals of Statistics, 19, 1-67.

Friedman J. (2001) Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29, 1189-1232.

Holmes C. and Held L. (2006) Bayesian auxiliary variable models for binary and multinomial regression. Bayesian Analysis, 1, 145-168. <doi:10.1214/06-ba105>.

Linero A. (2018) Bayesian regression trees for high dimensional prediction and variable selection. JASA, 113, 626-636. <doi:10.1080/01621459.2016.1264957>.

AFT BART for time-to-event outcomes

Description

BART is a Bayesian “sum-of-trees” model.
For a numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .

$f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

abart(
      x.train, times, delta,
      x.test=matrix(0,0,0), K=100,
      type='abart', ntype=1,
      sparse=FALSE, theta=0, omega=1,
      a=0.5, b=1, augment=FALSE, rho=NULL,
      xinfo=matrix(0,0,0), usequants=FALSE,
      rm.const=TRUE,
      sigest=NA, sigdf=3, sigquant=0.90,
      k=2, power=2, base=0.95,
      
      lambda=NA, tau.num=c(NA, 3, 6)[ntype], 
      offset=NULL, w=rep(1, length(times)),
      ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
      
      ndpost=1000L, nskip=100L, 
      keepevery=c(1L, 10L, 10L)[ntype],
      printevery=100L, transposed=FALSE,
      mc.cores = 1L, ## mc.abart only
      nice = 19L,    ## mc.abart only
      seed = 99L     ## mc.abart only
)

mc.abart(
         x.train, times, delta,
         x.test=matrix(0,0,0), K=100,
         type='abart', ntype=1,
         sparse=FALSE, theta=0, omega=1,
         a=0.5, b=1, augment=FALSE, rho=NULL,
         xinfo=matrix(0,0,0), usequants=FALSE,
         rm.const=TRUE,
         sigest=NA, sigdf=3, sigquant=0.90,
         k=2, power=2, base=0.95,
         
         lambda=NA, tau.num=c(NA, 3, 6)[ntype], 
         offset=NULL, w=rep(1, length(times)),
         
         ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
         ndpost=1000L, nskip=100L, 
         keepevery=c(1L, 10L, 10L)[ntype],
         printevery=100L, transposed=FALSE,
         mc.cores = 2L, nice = 19L, seed = 99L
)

abart(
      x.train, times, delta,
      x.test=matrix(0,0,0), K=100,
      type='abart', ntype=1,
      sparse=FALSE, theta=0, omega=1,
      a=0.5, b=1, augment=FALSE, rho=NULL,
      xinfo=matrix(0,0,0), usequants=FALSE,
      rm.const=TRUE,
      sigest=NA, sigdf=3, sigquant=0.90,
      k=2, power=2, base=0.95,
      
      lambda=NA, tau.num=c(NA, 3, 6)[ntype], 
      offset=NULL, w=rep(1, length(times)),
      ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
      
      ndpost=1000L, nskip=100L, 
      keepevery=c(1L, 10L, 10L)[ntype],
      printevery=100L, transposed=FALSE,
      mc.cores = 1L, ## mc.abart only
      nice = 19L,    ## mc.abart only
      seed = 99L     ## mc.abart only
)

mc.abart(
         x.train, times, delta,
         x.test=matrix(0,0,0), K=100,
         type='abart', ntype=1,
         sparse=FALSE, theta=0, omega=1,
         a=0.5, b=1, augment=FALSE, rho=NULL,
         xinfo=matrix(0,0,0), usequants=FALSE,
         rm.const=TRUE,
         sigest=NA, sigdf=3, sigquant=0.90,
         k=2, power=2, base=0.95,
         
         lambda=NA, tau.num=c(NA, 3, 6)[ntype], 
         offset=NULL, w=rep(1, length(times)),
         
         ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
         ndpost=1000L, nskip=100L, 
         keepevery=c(1L, 10L, 10L)[ntype],
         printevery=100L, transposed=FALSE,
         mc.cores = 2L, nice = 19L, seed = 99L
)

Arguments

`x.train`	Explanatory variables for training (in sample) data. May be a matrix or a data frame, with (as usual) rows corresponding to observations and columns to variables. If a variable is a factor in a data frame, it is replaced with dummies. Note that $q$ dummies are created if $q>2$ and one dummy created if $q=2$ where $q$ is the number of levels of the factor. `abart` will generate draws of $f(x)$ for each $x$ which is a row of `x.train`.
`times`	The time of event or right-censoring. If `y.train` is `NULL`, then `times` (and `delta`) must be provided.
`delta`	The event indicator: 1 is an event while 0 is censored. If `y.train` is `NULL`, then `delta` (and `times`) must be provided.
`x.test`	Explanatory variables for test (out of sample) data. Should have same structure as `x.train`. `abart` will generate draws of $f(x)$ for each $x$ which is a row of `x.test`.
`K`	If provided, then coarsen `times` per the quantiles $1/K, 2/K, ..., K/K$ .
`type`	You can use this argument to specify the type of fit. `'abart'` for AFT BART.
`ntype`	The integer equivalent of `type` where `'abart'` is 1.
`sparse`	Whether to perform variable selection based on a sparse Dirichlet prior rather than simply uniform; see Linero 2016.
`theta`	Set $theta$ parameter; zero means random.
`omega`	Set $omega$ parameter; zero means random.
`a`	Sparse parameter for $Beta(a, b)$ prior: $0.5<=a<=1$ where lower values inducing more sparsity.
`b`	Sparse parameter for $Beta(a, b)$ prior; typically, $b=1$ .
`rho`	Sparse parameter: typically $rho=p$ where $p$ is the number of covariates under consideration.
`augment`	Whether data augmentation is to be performed in sparse variable selection.
`xinfo`	You can provide the cutpoints to BART or let BART choose them for you. To provide them, use the `xinfo` argument to specify a list (matrix) where the items (rows) are the covariates and the contents of the items (columns) are the cutpoints.
`usequants`	If `usequants=FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, uniform quantiles are used for the cutpoints.
`rm.const`	Whether or not to remove constant variables.
`sigest`	The prior for the error variance ( $sigma^2$ ) is inverted chi-squared (the standard conditionally conjugate prior). The prior is specified by choosing the degrees of freedom, a rough estimate of the corresponding standard deviation and a quantile to put this rough estimate at. If `sigest=NA` then the rough estimate will be the usual least squares estimator. Otherwise the supplied value will be used. Not used if $y$ is binary.
`sigdf`	Degrees of freedom for error variance prior. Not used if $y$ is binary.
`sigquant`	The quantile of the prior that the rough estimate (see `sigest`) is placed at. The closer the quantile is to 1, the more aggresive the fit will be as you are putting more prior weight on error standard deviations ( $sigma$ ) less than the rough estimate. Not used if $y$ is binary.
`k`	For numeric $y$ , `k` is the number of prior standard deviations $E(Y\|x) = f(x)$ is away from +/-0.5. For binary $y$ , `k` is the number of prior standard deviations $f(x)$ is away from +/-3. The bigger `k` is, the more conservative the fitting will be.
`power`	Power parameter for tree prior.
`base`	Base parameter for tree prior.
`lambda`	The scale of the prior for the variance. Not used if $y$ is binary.
`tau.num`	The numerator in the `tau` definition, i.e., `tau=tau.num/(k*sqrt(ntree))`.
`offset`	Continous BART operates on `y.train` centered by `offset` which defaults to `mean(y.train)`. With binary BART, the centering is $P(Y=1 \| x) = F(f(x) + offset)$ where `offset` defaults to `F^{-1}(mean(y.train))`. You can use the `offset` parameter to over-ride these defaults.
`w`	Vector of weights which multiply the standard deviation. Not used if $y$ is binary.
`ntree`	The number of trees in the sum.
`numcut`	The number of possible values of $c$ (see `usequants`). If a single number if given, this is used for all variables. Otherwise a vector with length equal to `ncol(x.train)` is required, where the $i^{th}$ element gives the number of $c$ used for the $i^{th}$ variable in `x.train`. If usequants is false, numcut equally spaced cutoffs are used covering the range of values in the corresponding column of `x.train`. If `usequants` is true, then $min(numcut, the number of unique values in the corresponding columns of x.train - 1)$ values are used.
`ndpost`	The number of posterior draws returned.
`nskip`	Number of MCMC iterations to be treated as burn in.
`printevery`	As the MCMC runs, a message is printed every printevery draws.
`keepevery`	Every keepevery draw is kept to be returned to the user.
`transposed`	When running `abart` in parallel, it is more memory-efficient to transpose `x.train` and `x.test`, if any, prior to calling `mc.abart`.
`seed`	Setting the seed required for reproducible MCMC.
`mc.cores`	Number of cores to employ in parallel.
`nice`	Set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest).

Details

BART is a Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

Thus, unlike a lot of other modelling methods in R, we do not produce a single model object from which fits and summaries may be extracted. The output consists of values $f^*(x)$ (and $\sigma^*$ in the numeric case) where * denotes a particular draw. The $x$ is either a row from the training data, x.train or the test data, x.test.

Value

abart returns an object of type abart which is essentially a list. In the numeric $y$ case, the list has components:

`yhat.train`	A matrix with ndpost rows and nrow(x.train) columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to a row of x.train. The $(i,j)$ value is $f^(x)$ for the $i^{th}$ kept draw of $f$ and the $j^{th}$ row of x.train. Burn-in is dropped.
`yhat.test`	Same as yhat.train but now the x's are the rows of the test data.
`yhat.train.mean`	train data fits = mean of yhat.train columns.
`yhat.test.mean`	test data fits = mean of yhat.test columns.
`sigma`	post burn in draws of sigma, length = ndpost.
`first.sigma`	burn-in draws of sigma.
`varcount`	a matrix with ndpost rows and nrow(x.train) columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times that variable is used in a tree decision rule (over all trees) is given.
`sigest`	The rough error standard deviation ( $\sigma$ ) used in the prior.

Examples


N = 1000
P = 5       #number of covariates
M = 8

set.seed(12)
x.train=matrix(runif(N*P, -2, 2), N, P)
mu = x.train[ , 1]^3
y=rnorm(N, mu)
offset=mean(y)
T=exp(y)
C=rexp(N, 0.05)
delta=(T<C)*1
table(delta)/N
times=(T*delta+C*(1-delta))

##test BART with token run to ensure installation works
set.seed(99)
post1 = abart(x.train, times, delta, nskip=5, ndpost=10)

## Not run: 

post1 = mc.abart(x.train, times, delta,
                 mc.cores=M, seed=99)
post2 = mc.abart(x.train, times, delta, offset=offset,
                 mc.cores=M, seed=99)

Z=8

plot(mu, post1$yhat.train.mean, asp=1,
     xlim=c(-Z, Z), ylim=c(-Z, Z))
abline(a=0, b=1)

plot(mu, post2$yhat.train.mean, asp=1,
     xlim=c(-Z, Z), ylim=c(-Z, Z))
abline(a=0, b=1)

plot(post1$yhat.train.mean, post2$yhat.train.mean, asp=1,
     xlim=c(-Z, Z), ylim=c(-Z, Z))
abline(a=0, b=1)


## End(Not run)

N = 1000
P = 5       #number of covariates
M = 8

set.seed(12)
x.train=matrix(runif(N*P, -2, 2), N, P)
mu = x.train[ , 1]^3
y=rnorm(N, mu)
offset=mean(y)
T=exp(y)
C=rexp(N, 0.05)
delta=(T<C)*1
table(delta)/N
times=(T*delta+C*(1-delta))

##test BART with token run to ensure installation works
set.seed(99)
post1 = abart(x.train, times, delta, nskip=5, ndpost=10)

## Not run: 

post1 = mc.abart(x.train, times, delta,
                 mc.cores=M, seed=99)
post2 = mc.abart(x.train, times, delta, offset=offset,
                 mc.cores=M, seed=99)

Z=8

plot(mu, post1$yhat.train.mean, asp=1,
     xlim=c(-Z, Z), ylim=c(-Z, Z))
abline(a=0, b=1)

plot(mu, post2$yhat.train.mean, asp=1,
     xlim=c(-Z, Z), ylim=c(-Z, Z))
abline(a=0, b=1)

plot(post1$yhat.train.mean, post2$yhat.train.mean, asp=1,
     xlim=c(-Z, Z), ylim=c(-Z, Z))
abline(a=0, b=1)


## End(Not run)

AIDS Clinical Trials Group Study 175

Description

ACTG 175 was a randomized clinical trial to compare monotherapy with zidovudine or didanosine with combination therapy with zidovudine and didanosine or zidovudine and zalcitabine in adults infected with the human immunodeficiency virus type I whose CD4 T cell counts were between 200 and 500 per cubic millimeter.

Usage

data(ACTG175)data(ACTG175)

Format

A data frame with 2139 observations on the following 27 variables:

pidnum: patien ID number
age: age in years at baseline
wtkg: weight in kg at baseline
hemo: hemophilia (0=no, 1=yes)
homo: homosexual activity (0=no, 1=yes)
drugs: history of intravenous drug use (0=no, 1=yes)
karnof: Karnofsky score (on a scale of 0-100)
oprior: non-zidovudine antiretroviral therapy prior to initiation of study treatment (0=no, 1=yes)
z30: zidovudine use in the 30 days prior to treatment initiation (0=no, 1=yes)
zprior: zidovudine use prior to treatment initiation (0=no, 1=yes)
preanti: number of days of previously received antiretroviral therapy
race: race (0=white, 1=non-white)
gender: gender (0=female, 1=male)
str2: antiretroviral history (0=naive, 1=experienced)
strat: antiretroviral history stratification (1='antiretroviral naive', 2='> 1 but <= 52 weeks of prior antiretroviral therapy', 3='> 52 weeks')
symptom: symptomatic indicator (0=asymptomatic, 1=symptomatic)
treat: treatment indicator (0=zidovudine only, 1=other therapies)
offtrt: indicator of off-treatment before 96+/-5 weeks (0=no,1=yes)
cd40: CD4 T cell count at baseline
cd420: CD4 T cell count at 20+/-5 weeks
cd496: CD4 T cell count at 96+/-5 weeks (=NA if missing)
r: missing CD4 T cell count at 96+/-5 weeks (0=missing, 1=observed)
cd80: CD8 T cell count at baseline
cd820: CD8 T cell count at 20+/-5 weeks
cens: indicator of observing the event in days
days: number of days until the first occurrence of: (i) a decline in CD4 T cell count of at least 50 (ii) an event indicating progression to AIDS, or (iii) death.
arms: treatment arm (0=zidovudine, 1=zidovudine and didanosine, 2=zidovudine and zalcitabine, 3=didanosine).

Details

The variable days contains right-censored time-to-event observations. The data set includes the following post-randomization covariates: CD4 and CD8 T cell count at 20+/-5 weeks and the indicator of whether or not the patient was taken off-treatment before 96+/-5 weeks.

References

Hammer SM, et al. (1996) A trial comparing nucleoside monotherapy with combination therapy in HIV-infected adults with CD4 cell counts from 200 to 500 per cubic millimeter. New England Journal of Medicine 335, 1081-1090.

American alligator Food Choice

Description

In 1985, American alligators were harvested by hunters from August 26 to September 30 in peninsular Florida from lakes Oklawaha (Putnam County), George (Putnam and Volusia counties), Hancock (Polk County) and Trafford (Collier County). Lake, length and sex were recorded for each alligator. Stomachs from a sample of alligators 1.09-3.89m long were frozen prior to analysis. After thawing, stomach contents were removed and separated and food items were identified and tallied. Volumes were determined by water displacement. The stomach contents of 219 alligators were classified into five categories of primary food choice: Fish (the most common primary food choice), Invertebrate (snails, insects, crayfish, etc.), Reptile (turtles, alligators), Bird, and Other (amphibians, plants, household pets, stones, and other debris).

Usage

data(alligator)data(alligator)

Format

A data frame with 80 observations on the following 5 variables.

lake: a factor with levels George Hancock Oklawaha Trafford
sex: a factor with levels female male
size: alligator size, a factor with levels large (>2.3m) small (<=2.3m)
food: primary food choice, a factor with levels bird fish invert other reptile
count: cell frequency, a numeric vector

Details

The table contains a fair number of 0 counts. food is the response variable. fish is the most frequent choice, and often taken as a baseline category in multinomial response models.

Source

Agresti, A. (2002). Categorical Data Analysis, New York: Wiley, 2nd Ed., Table 7.1

References

Delany MF, Linda SB, Moore CT (1999). "Diet and condition of American alligators in 4 Florida lakes." In Proceedings of the Annual Conference of the Southeastern Association of Fish and Wildlife Agencies, 53, 375–389.

Examples


data(alligator)
     
## Not run: 
library(nnet)    
## nnet::multinom Multinomial logit model fit with neural nets
fit <- multinom(food ~ lake+size+sex, data=alligator, weights=count)

summary(fit$fitted.values)
## 1=bird, 2=fish, 3=invert, 4=other, 5=reptile

(L=length(alligator$count))
(N=sum(alligator$count))
y.train=integer(N)
x.train=matrix(nrow=N, ncol=3)
x.test=matrix(nrow=L, ncol=3)
k=1
for(i in 1:L) {
    x.test[i, ]=as.integer(
        c(alligator$lake[i], alligator$size[i], alligator$sex[i]))
    if(alligator$count[i]>0)
        for(j in 1:alligator$count[i]) {
            y.train[k]=as.integer(alligator$food[i])
            x.train[k, ]=as.integer(
                c(alligator$lake[i], alligator$size[i], alligator$sex[i]))
            k=k+1
        }
}
table(y.train)
##test mbart with token run to ensure installation works
set.seed(99)
check = mbart(x.train, y.train, nskip=1, ndpost=1)

set.seed(99)
check = mbart(x.train, y.train, nskip=1, ndpost=1)
post=mbart(x.train, y.train, x.test)

##post=mc.mbart(x.train, y.train, x.test, mc.cores=8, seed=99)
##check=predict(post, x.test, mc.cores=8)
##print(cor(post$prob.test.mean, check$prob.test.mean)^2)

par(mfrow=c(3, 2))
K=5
for(j in 1:5) {
    h=seq(j, L*K, K)
    print(cor(fit$fitted.values[ , j], post$prob.test.mean[h])^2)
    plot(fit$fitted.values[ , j], post$prob.test.mean[h],
         xlim=0:1, ylim=0:1,
         xlab=paste0('NN: Est. Prob. j=', j),
         ylab=paste0('BART: Est. Prob. j=', j))
    abline(a=0, b=1)
}
par(mfrow=c(1, 1))

L=16
x.test=matrix(nrow=L, ncol=3)
k=1
for(size in 1:2)
    for(sex in 1:2)
        for(lake in 1:4) {
            x.test[k, ]=c(lake, size, sex)
            k=k+1
        }
x.test

## two sizes: 1=large: >2.3m, 2=small: <=2.3m
pred=predict(post, x.test)
##pred=predict(post, x.test, mc.cores=8)
ndpost=nrow(pred$prob.test)

size.test=matrix(nrow=ndpost, ncol=K*2)
for(i in 1:K) {
    j=seq(i, L*K/2, K) ## large
    size.test[ , i]=apply(pred$prob.test[ , j], 1, mean)
    j=j+L*K/2 ## small
    size.test[ , i+K]=apply(pred$prob.test[ , j], 1, mean)
}
size.test.mean=apply(size.test, 2, mean)
size.test.025=apply(size.test, 2, quantile, probs=0.025)
size.test.975=apply(size.test, 2, quantile, probs=0.975)

plot(factor(1:K, labels=c('bird', 'fish', 'invert', 'other', 'reptile')),
     rep(1, K), col=1:K, type='n', lwd=1, lty=0,
             xlim=c(1, K), ylim=c(0, 0.5), ylab='Prob.',
     sub="Multinomial BART\nFriedman's partial dependence function")
points(1:K, size.test.mean[1:K+K], col=1)
lines(1:K, size.test.025[1:K+K], col=1, lty=2)
lines(1:K, size.test.975[1:K+K], col=1, lty=2)
points(1:K, size.test.mean[1:K], col=2)
lines(1:K, size.test.025[1:K], col=2, lty=2)
lines(1:K, size.test.975[1:K], col=2, lty=2)
## legend('topright', legend=c('Small', 'Large'),
##        pch=1, col=1:2)


## End(Not run)
data(alligator)
     
## Not run: 
library(nnet)    
## nnet::multinom Multinomial logit model fit with neural nets
fit <- multinom(food ~ lake+size+sex, data=alligator, weights=count)

summary(fit$fitted.values)
## 1=bird, 2=fish, 3=invert, 4=other, 5=reptile

(L=length(alligator$count))
(N=sum(alligator$count))
y.train=integer(N)
x.train=matrix(nrow=N, ncol=3)
x.test=matrix(nrow=L, ncol=3)
k=1
for(i in 1:L) {
    x.test[i, ]=as.integer(
        c(alligator$lake[i], alligator$size[i], alligator$sex[i]))
    if(alligator$count[i]>0)
        for(j in 1:alligator$count[i]) {
            y.train[k]=as.integer(alligator$food[i])
            x.train[k, ]=as.integer(
                c(alligator$lake[i], alligator$size[i], alligator$sex[i]))
            k=k+1
        }
}
table(y.train)
##test mbart with token run to ensure installation works
set.seed(99)
check = mbart(x.train, y.train, nskip=1, ndpost=1)

set.seed(99)
check = mbart(x.train, y.train, nskip=1, ndpost=1)
post=mbart(x.train, y.train, x.test)

##post=mc.mbart(x.train, y.train, x.test, mc.cores=8, seed=99)
##check=predict(post, x.test, mc.cores=8)
##print(cor(post$prob.test.mean, check$prob.test.mean)^2)

par(mfrow=c(3, 2))
K=5
for(j in 1:5) {
    h=seq(j, L*K, K)
    print(cor(fit$fitted.values[ , j], post$prob.test.mean[h])^2)
    plot(fit$fitted.values[ , j], post$prob.test.mean[h],
         xlim=0:1, ylim=0:1,
         xlab=paste0('NN: Est. Prob. j=', j),
         ylab=paste0('BART: Est. Prob. j=', j))
    abline(a=0, b=1)
}
par(mfrow=c(1, 1))

L=16
x.test=matrix(nrow=L, ncol=3)
k=1
for(size in 1:2)
    for(sex in 1:2)
        for(lake in 1:4) {
            x.test[k, ]=c(lake, size, sex)
            k=k+1
        }
x.test

## two sizes: 1=large: >2.3m, 2=small: <=2.3m
pred=predict(post, x.test)
##pred=predict(post, x.test, mc.cores=8)
ndpost=nrow(pred$prob.test)

size.test=matrix(nrow=ndpost, ncol=K*2)
for(i in 1:K) {
    j=seq(i, L*K/2, K) ## large
    size.test[ , i]=apply(pred$prob.test[ , j], 1, mean)
    j=j+L*K/2 ## small
    size.test[ , i+K]=apply(pred$prob.test[ , j], 1, mean)
}
size.test.mean=apply(size.test, 2, mean)
size.test.025=apply(size.test, 2, quantile, probs=0.025)
size.test.975=apply(size.test, 2, quantile, probs=0.975)

plot(factor(1:K, labels=c('bird', 'fish', 'invert', 'other', 'reptile')),
     rep(1, K), col=1:K, type='n', lwd=1, lty=0,
             xlim=c(1, K), ylim=c(0, 0.5), ylab='Prob.',
     sub="Multinomial BART\nFriedman's partial dependence function")
points(1:K, size.test.mean[1:K+K], col=1)
lines(1:K, size.test.025[1:K+K], col=1, lty=2)
lines(1:K, size.test.975[1:K+K], col=1, lty=2)
points(1:K, size.test.mean[1:K], col=2)
lines(1:K, size.test.025[1:K], col=2, lty=2)
lines(1:K, size.test.975[1:K], col=2, lty=2)
## legend('topright', legend=c('Small', 'Large'),
##        pch=1, col=1:2)


## End(Not run)

NHANES 2009-2010 Arthritis Questionnaire

Description

This data set was created from the National Health and Nutrition Examination Survey (NHANES) 2009-2010 Arthritis Questionnaire.

Usage

data(arq)
data(arq)

Details

We have two outcomes of interest. Chronic neck pain: Yes arq010a=1 vs.\ No arq010a=0. Chronic lower-back/buttock pain: Yes arq010de=1 vs.\ No arq010de=0. seqn is a unique survey respondent identifier. wtint2yr is the survey sampling weight. riagendr is gender: 1 for males, 2 for females. ridageyr is age in years. There are several anthropometric measurements: bmxwt, weight in kg; bmxht, height in cm; bmxbmi, body mass index in kg/ $m^2$ ; and bmxwaist, waist circumference in cm. The data was subsetted to ensure non-missing values of these variables.

References

National Health and Nutrition Examination Survey (NHANES) 2009-2010 Arthritis Questionnaire. https://wwwn.cdc.gov/nchs/nhanes/2009-2010/ARQ_F.htm

Create a matrix out of a vector or data.frame

Description

The external BART functions operate on matrices in memory. Therefore, if the user submits a vector or data.frame, then this function converts it to a matrix. Also, it determines the number of cutpoints necessary for each column when asked to do so.

Usage

bartModelMatrix(X, numcut=0L, usequants=FALSE, type=7,
                rm.const=FALSE, cont=FALSE, xinfo=NULL)
bartModelMatrix(X, numcut=0L, usequants=FALSE, type=7,
                rm.const=FALSE, cont=FALSE, xinfo=NULL)

Arguments

`X`	A vector or data.frame to create the matrix from.
`numcut`	The maximum number of cutpoints to consider. If `numcut=0`, then just return a matrix; otherwise, return a list containing a matrix `X`, a vector `numcut` and a list `xinfo`.
`usequants`	If `usequants` is `FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, then quantiles are used for the cutpoints.
`type`	Determines which quantile algorithm is employed.
`rm.const`	Whether or not to remove constant variables.
`cont`	Whether or not to assume all variables are continuous.
`xinfo`	You can provide the cutpoints to BART or let BART choose them for you. To provide them, use the `xinfo` argument to specify a list (matrix) where the items (rows) are the covariates and the contents of the items (columns) are the cutpoints.

Examples


set.seed(99)

a <- rbinom(10, 4, 0.4)

table(a)

x <- runif(10)

df <- data.frame(a=factor(a), x=x)

b <- bartModelMatrix(df)

b

b <- bartModelMatrix(df, numcut=9)

b

b <- bartModelMatrix(df, numcut=9, usequants=TRUE)

b

## Not run: 
    f <- bartModelMatrix(as.character(a))

## End(Not run)
set.seed(99)

a <- rbinom(10, 4, 0.4)

table(a)

x <- runif(10)

df <- data.frame(a=factor(a), x=x)

b <- bartModelMatrix(df)

b

b <- bartModelMatrix(df, numcut=9)

b

b <- bartModelMatrix(df, numcut=9, usequants=TRUE)

b

## Not run: 
    f <- bartModelMatrix(as.character(a))

## End(Not run)

Bladder Cancer Recurrences

Description

This interesting example is from a clinical trial conducted by the Veterans Administration Cooperative Urological Research Group. This data on recurrence of bladder cancer has been used by many to demonstrate methodology for recurrent events modelling. In this study, all patients had superficial bladder tumors when they entered the trial. These tumors were removed transurethrally and patients were randomly assigned to one of three treatments: placebo, thiotepa or pyridoxine (vitamin B6). Many patients had multiple recurrences of tumors during the study and new tumors were removed at each visit. For each patient, their recurrence time, if any, was measured from the beginning of treatment.

bladder is the data set that appears most commonly in the literature. It uses only the 85 subjects with nonzero follow-up who were assigned to either thiotepa or placebo and only the first four recurrences for any patient. The status variable is 1 for recurrence and 0 for everything else (including death for any reason). The data set is laid out in the competing risks format of the paper by Wei, Lin, and Weissfeld (WLW).

bladder1 is the full data set from the study. It contains all three treatment arms and all recurrences for 118 subjects; the maximum observed number of recurrences is 9.

bladder2 uses the same subset of subjects as bladder, but formated in the (start, stop] or Anderson-Gill (AG) style. Note that in transforming from the WLW to the AG style data set there is a quite common programming mistake that leads to extra follow-up time for 12 subjects: all those with follow-up beyond their fourth recurrence. Over this extended time these subjects are by definition not at risk for another event in the WLW data set.

Format

bladder

id:	Patient id
rx:	Treatment 1=placebo 2=thiotepa
number:	Initial number of tumours (8=8 or more)
size:	size (cm) of largest initial tumour
stop:	recurrence or censoring time
enum:	which recurrence (up to 4)

bladder1

id:	Patient id
treatment:	Placebo, pyridoxine (vitamin B6), or thiotepa
number:	Initial number of tumours (8=8 or more)
size:	Size (cm) of largest initial tumour
recur:	Number of recurrences
start,stop:	The start and end time of each time interval
status:	End of interval code, 0=censored, 1=recurrence,
	2=death from bladder disease, 3=death other/unknown cause
rtumor:	Number of tumors found at the time of a recurrence
rsize:	Size of largest tumor at a recurrence
enum:	Event number (observation number within patient)

bladder2

id:	Patient id
rx:	Treatment 1=placebo 2=thiotepa
number:	Initial number of tumours (8=8 or more)
size:	size (cm) of largest initial tumour
start:	start of interval (0 or previous recurrence time)
stop:	recurrence or censoring time
enum:	which recurrence (up to 4)

References

Byar, DP (1980), "The Veterans Administration Study of Chemoprophylaxis for Recurrent Stage I Bladder Tumors: Comparisons of Placebo, Pyridoxine, and Topical Thiotepa," in Bladder Tumors and Other Topics in Urological Oncology, eds. M Pavone-Macaluso, PH Smith, and F Edsmyn, New York: Plenum, pp. 363-370.

Andrews DF, Hertzberg AM (1985), DATA: A Collection of Problems from Many Fields for the Student and Research Worker, New York: Springer-Verlag.

LJ Wei, DY Lin, L Weissfeld (1989), Regression analysis of multivariate incomplete failure time data by modeling marginal distributions. Journal of the American Statistical Association, 84.

Examples

  data(bladder)
data(bladder)

Generates Class Indicator Matrix from a Factor

Description

Generates a class indicator function from a given factor.

Usage

class.ind(cl)
class.ind(cl)

Arguments

`cl`	factor or vector of classes for cases.

Value

a matrix which is zero except for the column corresponding to the class.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Examples


set.seed(99)

a <- rbinom(20, 4, 0.5)

table(a)

b <- class.ind(a)

str(b)

t(cbind(a, b))

set.seed(99)

a <- rbinom(20, 4, 0.5)

table(a)

b <- class.ind(a)

str(b)

t(cbind(a, b))

BART for competing risks

Description

Here we have implemented a simple and direct approach to utilize BART for competing risks that is very flexible, and is akin to discrete-time survival analysis. Following the capabilities of BART, we allow for maximum flexibility in modeling the dependence of competing failure times on covariates. In particular, we do not impose proportional hazards.

We denote the $K$ distinct event/censoring times by $0<t_{(1)}<...<t_{(K)}<\infty$ thus taking $t_{(j)}$ to be the $j^{th}$ order statistic among distinct observation times and, for convenience, $t_{(0)}=0$ . Now consider event indicators for cause $h$ : $y_{hij}$ for each subject $i$ at each distinct time $t_{(j)}$ up to and including the subject's last observation time $s_i=t_{(n_i)}$ with $n_i=\arg \max_j [t_{(j)}\leq s_i]$ for cause 1, but only up to $n_i-y_{1ij}$ for cause 2.

We then denote by $p_{hij}$ the probability of an event at time $t_{(j)}$ conditional on no previous event. We now write the model for $y_{hij}$ as a nonparametric probit (or logistic) regression of $y_{hij}$ on the time $t_{(j)}$ and the covariates ${x}_{hi}$ , and then utilize BART for binary responses. Specifically, $y_{hij}\ =\ I[\delta_i=h] I[s_i=t_{(j)}],\ j=1, ..., n_i-I[h=2]y_{1ij}$ . Therefore, we have $p_{hij} = F(mu_{hij}),\ mu_{hij} = mu_h+f_h(t_{(j)}, {x}_{hi})$ where $F$ denotes the Normal (or Logistic) cdf. As in the binary response case, $f_h$ is the sum of many tree models. Finally, based on these probabilities, $p_{hij}$ , we can construct targets of inference such as the cumulative incidence functions.

Usage


crisk.bart(x.train=matrix(0,0,0), y.train=NULL,
           x.train2=x.train, y.train2=NULL,
           times=NULL, delta=NULL, K=NULL,
           x.test=matrix(0,0,0), x.test2=x.test, cond=NULL,
           sparse=FALSE, theta=0, omega=1,
           a=0.5, b=1, augment=FALSE,
           rho=NULL, rho2=NULL,
           xinfo=matrix(0,0,0), xinfo2=matrix(0,0,0),
           usequants=FALSE, 
           rm.const=TRUE, type='pbart',
           ntype=as.integer(
               factor(type, levels=c('wbart', 'pbart', 'lbart'))),
           k=2, power=2, base=0.95,
           offset=NULL, offset2=NULL,
           tau.num=c(NA, 3, 6)[ntype], 
           
           ntree=50, numcut=100, ndpost=1000, nskip=250,
           keepevery = 10L,
           
           
           
           printevery=100L, 
           
           id=NULL,    ## crisk.bart only
           seed=99,    ## mc.crisk.bart only
           mc.cores=2, ## mc.crisk.bart only
           nice=19L    ## mc.crisk.bart only
          )

mc.crisk.bart(x.train=matrix(0,0,0), y.train=NULL,
              x.train2=x.train, y.train2=NULL,
              times=NULL, delta=NULL, K=NULL,
              x.test=matrix(0,0,0), x.test2=x.test, cond=NULL,
              sparse=FALSE, theta=0, omega=1,
              a=0.5, b=1, augment=FALSE,
              rho=NULL, rho2=NULL,
              xinfo=matrix(0,0,0), xinfo2=matrix(0,0,0),
              usequants=FALSE, 
              rm.const=TRUE, type='pbart',
              ntype=as.integer(
                  factor(type, levels=c('wbart', 'pbart', 'lbart'))),
              k=2, power=2, base=0.95,
              offset=NULL, offset2=NULL,
              tau.num=c(NA, 3, 6)[ntype], 
              
              ntree=50, numcut=100, ndpost=1000, nskip=250,
              keepevery = 10L,
              
              
              
              printevery=100L, 
              
              id=NULL,    ## crisk.bart only
              seed=99,    ## mc.crisk.bart only
              mc.cores=2, ## mc.crisk.bart only
              nice=19L    ## mc.crisk.bart only
             )
crisk.bart(x.train=matrix(0,0,0), y.train=NULL,
           x.train2=x.train, y.train2=NULL,
           times=NULL, delta=NULL, K=NULL,
           x.test=matrix(0,0,0), x.test2=x.test, cond=NULL,
           sparse=FALSE, theta=0, omega=1,
           a=0.5, b=1, augment=FALSE,
           rho=NULL, rho2=NULL,
           xinfo=matrix(0,0,0), xinfo2=matrix(0,0,0),
           usequants=FALSE, 
           rm.const=TRUE, type='pbart',
           ntype=as.integer(
               factor(type, levels=c('wbart', 'pbart', 'lbart'))),
           k=2, power=2, base=0.95,
           offset=NULL, offset2=NULL,
           tau.num=c(NA, 3, 6)[ntype], 
           
           ntree=50, numcut=100, ndpost=1000, nskip=250,
           keepevery = 10L,
           
           
           
           printevery=100L, 
           
           id=NULL,    ## crisk.bart only
           seed=99,    ## mc.crisk.bart only
           mc.cores=2, ## mc.crisk.bart only
           nice=19L    ## mc.crisk.bart only
          )

mc.crisk.bart(x.train=matrix(0,0,0), y.train=NULL,
              x.train2=x.train, y.train2=NULL,
              times=NULL, delta=NULL, K=NULL,
              x.test=matrix(0,0,0), x.test2=x.test, cond=NULL,
              sparse=FALSE, theta=0, omega=1,
              a=0.5, b=1, augment=FALSE,
              rho=NULL, rho2=NULL,
              xinfo=matrix(0,0,0), xinfo2=matrix(0,0,0),
              usequants=FALSE, 
              rm.const=TRUE, type='pbart',
              ntype=as.integer(
                  factor(type, levels=c('wbart', 'pbart', 'lbart'))),
              k=2, power=2, base=0.95,
              offset=NULL, offset2=NULL,
              tau.num=c(NA, 3, 6)[ntype], 
              
              ntree=50, numcut=100, ndpost=1000, nskip=250,
              keepevery = 10L,
              
              
              
              printevery=100L, 
              
              id=NULL,    ## crisk.bart only
              seed=99,    ## mc.crisk.bart only
              mc.cores=2, ## mc.crisk.bart only
              nice=19L    ## mc.crisk.bart only
             )

Arguments

`x.train`	Covariates for training (in sample) data of cause 1. Must be a data.frame or a matrix with rows corresponding to observations and columns to variables. `crisk.bart` will generate draws of $f_1(t, x)$ for each $x$ which is a row of `x.train` (note that the definition of `x.train` is dependent on whether `y.train` has been specified; see below).
`y.train`	Cause 1 binary response for training (in sample) data. If `y.train` is `NULL`, then `y.train` (`x.train` and `x.test`, if specified) are generated by a call to `crisk.pre.bart` (which require that `times` and `delta` be provided: see below); otherwise, `y.train` (`x.train` and `x.test`, if specified) are utilized as given assuming that the data construction has already been performed.
`x.train2`	Covariates for training (in sample) data of cause 2. Similar to `x.train` above.
`y.train2`	Cause 2 binary response for training (in sample) data, i.e., failure from any cause besides the cause of interest which is cause 1. Similar to `y.train` above.
`times`	The time of event or right-censoring, $s_i$ . If `y.train` is `NULL`, then `times` (and `delta`) must be provided.
`delta`	The event indicator: 1 for cause 1, 2 for cause 2 and 0 is censored. If `y.train` is `NULL`, then `delta` (and `times`) must be provided.
`K`	If provided, then coarsen `times` per the quantiles $1/K, 2/K, ..., K/K$ .
`x.test`	Covariates for test (out of sample) data of cause 1. Must be a data.frame or a matrix and have the same structure as `x.train`. `crisk.bart` will generate draws of $f_1(t, x)$ for each $x$ which is a row of `x.test`.
`x.test2`	Covariates for test (out of sample) data of cause 2. Similar to `x.test` above.
`cond`	A vector of indices for `y.train2` indicating subjects who did not suffer a cause 1 event and, therefore, are eligible for cause 2.
`sparse`	Whether to perform variable selection based on a sparse Dirichlet prior; see Linero 2016.
`theta`	Set $theta$ parameter; zero means random.
`omega`	Set $omega$ parameter; zero means random.
`a`	Sparse parameter for $Beta(a, b)$ prior: $0.5<=a<=1$ where lower values inducing more sparsity.
`b`	Sparse parameter for $Beta(a, b)$ prior; typically, `b=1`.
`rho`	Sparse parameter: typically `rho=p` where `p` is the number of covariates in `x.train`.
`rho2`	Sparse parameter: typically `rho2=p` where `p` is the number of covariates in `x.train2`.
`augment`	Whether data augmentation is to be performed in sparse variable selection.
`xinfo`	You can provide the cutpoints to BART or let BART choose them for you. To provide them, use the `xinfo` argument to specify a list (matrix) where the items (rows) are the covariates and the contents of the items (columns) are the cutpoints.
`xinfo2`	Cause 2 cutpoints.
`usequants`	If `usequants=FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, uniform quantiles are used for the cutpoints.
`rm.const`	Whether or not to remove constant variables.
`type`	Whether to employ probit BART via Albert-Chib, `'pbart'`, or logistic BART by Holmes-Held, `'lbart'`.
`ntype`	The integer equivalent of `type` where `'wbart'` is 1, `'pbart'` is 2 and `'lbart'` is 3.
`k`	k is the number of prior standard deviations $f_h(t, x)$ is away from +/-3. The bigger k is, the more conservative the fitting will be.
`power`	Power parameter for tree prior.
`base`	Base parameter for tree prior.
`offset`	Cause 1 binary offset.
`offset2`	Cause 2 binary offset.
`tau.num`	The numerator in the `tau` definition.
`ntree`	The number of trees in the sum.
`numcut`	The number of possible values of cutpoints (see `usequants`). If a single number if given, this is used for all variables. Otherwise a vector with length equal to `ncol(x.train)` is required, where the $i^{th}$ element gives the number of cutpoints used for the $i^{th}$ variable in `x.train`. If `usequants` is `FALSE`, `numcut` equally spaced cutoffs are used covering the range of values in the corresponding column of `x.train`. If `usequants` is `TRUE`, then `min(numcut, the number of unique values in the corresponding columns of x.train - 1)` cutpoint values are used.
`ndpost`	The number of posterior draws returned.
`nskip`	Number of MCMC iterations to be treated as burn in.
`keepevery`	Every `keepevery` draw is kept to be returned to the user.
`printevery`	As the MCMC runs, a message is printed every `printevery` draws.
`id`	`crisk.bart` only: unique identifier added to returned list.
`seed`	`mc.crisk.bart` only: seed required for reproducible MCMC.
`mc.cores`	`mc.crisk.bart` only: number of cores to employ in parallel.
`nice`	`mc.crisk.bart` only: set the job niceness. The default niceness is 19: niceness goes from 0 (highest priority) to 19 (lowest priority).

Value

crisk.bart returns an object of type criskbart which is essentially a list. Besides the items listed below, the list has offset, offset2, times which are the unique times, K which is the number of unique times, tx.train and tx.test, if any.

`yhat.train`	A matrix with `ndpost` rows and `nrow(x.train)` columns. Each row corresponds to a draw $f^_1$ from the posterior of $f_1$ and each column corresponds to a row of `x.train`. The $(i,j)$ value is $f^_1(t, x)$ for the $i^{th}$ kept draw of $f_1$ and the $j^{th}$ row of `x.train`. Burn-in is dropped.
`yhat.test`	Same as `yhat.train` but now the x's are the rows of the test data.
`surv.test`	test data fits for the survival function, $S(t, x)$ .
`surv.test.mean`	mean of `surv.test` over the posterior samples.
`prob.test`	The probability of suffering cause 1.
`prob.test2`	The probability of suffering cause 2.
`cif.test`	The cumulative incidence function of cause 1, $F_1(t, x)$ .
`cif.test2`	The cumulative incidence function of cause 2, $F_2(t, x)$ .
`cif.test.mean`	mean of `cif.test` columns for cause 1.
`cif.test2.mean`	mean of `cif.test2` columns for cause 2.
`varcount`	a matrix with `ndpost` rows and `nrow(x.train)` columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times this variable is used for cause 1 in a tree decision rule (over all trees) is given.
`varcount2`	For each variable the total count of the number of times this variable is used for cause 2 in a tree decision rule is given.

Examples


data(transplant)

pfit <- survfit(Surv(futime, event) ~ abo, transplant)

# competing risks for type O
plot(pfit[4,], xscale=7, xmax=735, col=1:3, lwd=2, ylim=c(0, 1),
       xlab='t (weeks)', ylab='Aalen-Johansen (AJ) CI(t)')
    legend(450, .4, c("Death", "Transplant", "Withdrawal"), col=1:3, lwd=2)
## plot(pfit[4,], xscale=30.5, xmax=735, col=1:3, lwd=2, ylim=c(0, 1),
##        xlab='t (months)', ylab='Aalen-Johansen (AJ) CI(t)')
##     legend(450, .4, c("Death", "Transplant", "Withdrawal"), col=1:3, lwd=2)

delta <- (as.numeric(transplant$event)-1)
## recode so that delta=1 is cause of interest; delta=2 otherwise
delta[delta==1] <- 4
delta[delta==2] <- 1
delta[delta>1] <- 2
table(delta, transplant$event)

times <- pmax(1, ceiling(transplant$futime/7)) ## weeks
##times <- pmax(1, ceiling(transplant$futime/30.5)) ## months
table(times)

typeO <- 1*(transplant$abo=='O')
typeA <- 1*(transplant$abo=='A')
typeB <- 1*(transplant$abo=='B')
typeAB <- 1*(transplant$abo=='AB')
table(typeA, typeO)

x.train <- cbind(typeO, typeA, typeB, typeAB)

x.test <- cbind(1, 0, 0, 0)
dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

##test BART with token run to ensure installation works
set.seed(99)
post <- crisk.bart(x.train=x.train, times=times, delta=delta,
                   x.test=x.test, nskip=1, ndpost=1, keepevery=1)

## Not run: 

## run one long MCMC chain in one process
## set.seed(99)
## post <- crisk.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

## in the interest of time, consider speeding it up by parallel processing
## run "mc.cores" number of shorter MCMC chains in parallel processes
post <- mc.crisk.bart(x.train=x.train, times=times, delta=delta,
                      x.test=x.test, seed=99, mc.cores=8)

K <- post$K

typeO.cif.mean <- apply(post$cif.test, 2, mean)
typeO.cif.025 <- apply(post$cif.test, 2, quantile, probs=0.025)
typeO.cif.975 <- apply(post$cif.test, 2, quantile, probs=0.975)

plot(pfit[4,], xscale=7, xmax=735, col=1:3, lwd=2, ylim=c(0, 0.8),
       xlab='t (weeks)', ylab='CI(t)')
points(c(0, post$times)*7, c(0, typeO.cif.mean), col=4, type='s', lwd=2)
points(c(0, post$times)*7, c(0, typeO.cif.025), col=4, type='s', lwd=2, lty=2)
points(c(0, post$times)*7, c(0, typeO.cif.975), col=4, type='s', lwd=2, lty=2)
     legend(450, .4, c("Transplant(BART)", "Transplant(AJ)",
                       "Death(AJ)", "Withdrawal(AJ)"),
            col=c(4, 2, 1, 3), lwd=2)
##dev.copy2pdf(file='../vignettes/figures/liver-BART.pdf')
## plot(pfit[4,], xscale=30.5, xmax=735, col=1:3, lwd=2, ylim=c(0, 0.8),
##        xlab='t (months)', ylab='CI(t)')
## points(c(0, post$times)*30.5, c(0, typeO.cif.mean), col=4, type='s', lwd=2)
## points(c(0, post$times)*30.5, c(0, typeO.cif.025), col=4, type='s', lwd=2, lty=2)
## points(c(0, post$times)*30.5, c(0, typeO.cif.975), col=4, type='s', lwd=2, lty=2)
##      legend(450, .4, c("Transplant(BART)", "Transplant(AJ)",
##                        "Death(AJ)", "Withdrawal(AJ)"),
##             col=c(4, 2, 1, 3), lwd=2)


## End(Not run)
data(transplant)

pfit <- survfit(Surv(futime, event) ~ abo, transplant)

# competing risks for type O
plot(pfit[4,], xscale=7, xmax=735, col=1:3, lwd=2, ylim=c(0, 1),
       xlab='t (weeks)', ylab='Aalen-Johansen (AJ) CI(t)')
    legend(450, .4, c("Death", "Transplant", "Withdrawal"), col=1:3, lwd=2)
## plot(pfit[4,], xscale=30.5, xmax=735, col=1:3, lwd=2, ylim=c(0, 1),
##        xlab='t (months)', ylab='Aalen-Johansen (AJ) CI(t)')
##     legend(450, .4, c("Death", "Transplant", "Withdrawal"), col=1:3, lwd=2)

delta <- (as.numeric(transplant$event)-1)
## recode so that delta=1 is cause of interest; delta=2 otherwise
delta[delta==1] <- 4
delta[delta==2] <- 1
delta[delta>1] <- 2
table(delta, transplant$event)

times <- pmax(1, ceiling(transplant$futime/7)) ## weeks
##times <- pmax(1, ceiling(transplant$futime/30.5)) ## months
table(times)

typeO <- 1*(transplant$abo=='O')
typeA <- 1*(transplant$abo=='A')
typeB <- 1*(transplant$abo=='B')
typeAB <- 1*(transplant$abo=='AB')
table(typeA, typeO)

x.train <- cbind(typeO, typeA, typeB, typeAB)

x.test <- cbind(1, 0, 0, 0)
dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

##test BART with token run to ensure installation works
set.seed(99)
post <- crisk.bart(x.train=x.train, times=times, delta=delta,
                   x.test=x.test, nskip=1, ndpost=1, keepevery=1)

## Not run: 

## run one long MCMC chain in one process
## set.seed(99)
## post <- crisk.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

## in the interest of time, consider speeding it up by parallel processing
## run "mc.cores" number of shorter MCMC chains in parallel processes
post <- mc.crisk.bart(x.train=x.train, times=times, delta=delta,
                      x.test=x.test, seed=99, mc.cores=8)

K <- post$K

typeO.cif.mean <- apply(post$cif.test, 2, mean)
typeO.cif.025 <- apply(post$cif.test, 2, quantile, probs=0.025)
typeO.cif.975 <- apply(post$cif.test, 2, quantile, probs=0.975)

plot(pfit[4,], xscale=7, xmax=735, col=1:3, lwd=2, ylim=c(0, 0.8),
       xlab='t (weeks)', ylab='CI(t)')
points(c(0, post$times)*7, c(0, typeO.cif.mean), col=4, type='s', lwd=2)
points(c(0, post$times)*7, c(0, typeO.cif.025), col=4, type='s', lwd=2, lty=2)
points(c(0, post$times)*7, c(0, typeO.cif.975), col=4, type='s', lwd=2, lty=2)
     legend(450, .4, c("Transplant(BART)", "Transplant(AJ)",
                       "Death(AJ)", "Withdrawal(AJ)"),
            col=c(4, 2, 1, 3), lwd=2)
##dev.copy2pdf(file='../vignettes/figures/liver-BART.pdf')
## plot(pfit[4,], xscale=30.5, xmax=735, col=1:3, lwd=2, ylim=c(0, 0.8),
##        xlab='t (months)', ylab='CI(t)')
## points(c(0, post$times)*30.5, c(0, typeO.cif.mean), col=4, type='s', lwd=2)
## points(c(0, post$times)*30.5, c(0, typeO.cif.025), col=4, type='s', lwd=2, lty=2)
## points(c(0, post$times)*30.5, c(0, typeO.cif.975), col=4, type='s', lwd=2, lty=2)
##      legend(450, .4, c("Transplant(BART)", "Transplant(AJ)",
##                        "Death(AJ)", "Withdrawal(AJ)"),
##             col=c(4, 2, 1, 3), lwd=2)


## End(Not run)

Data construction for competing risks with BART

Description

Competing risks contained in $(t, \delta, x)$ must be translated to data suitable for the BART competing risks model; see crisk.bart for more details.

Usage

crisk.pre.bart( times, delta, x.train=NULL, x.test=NULL,
                x.train2=x.train, x.test2=x.test, K=NULL )
crisk.pre.bart( times, delta, x.train=NULL, x.test=NULL,
                x.train2=x.train, x.test2=x.test, K=NULL )

Arguments

`times`	The time of event or right-censoring.
`delta`	The event indicator: 1 is a cause 1 event, 2 a cause 2 while 0 is censored.
`x.train`	Explanatory variables for training (in sample) data of cause 1. If provided, must be a matrix with (as usual) rows corresponding to observations and columns to variables.
`x.test`	Explanatory variables for test (out of sample) data of cause 1. If provided, must be a matrix and have the same structure as x.train.
`x.train2`	Explanatory variables for training (in sample) data of cause 2. If provided, must be a matrix with (as usual) rows corresponding to observations and columns to variables.
`x.test2`	Explanatory variables for test (out of sample) data of cause 2. If provided, must be a matrix and have the same structure as x.train.
`K`	If provided, then coarsen `times` per the quantiles $1/K, 2/K, ..., K/K$ .

Value

surv.pre.bart returns a list. Besides the items listed below, the list has a times component giving the unique times and K which is the number of unique times.

`y.train`	A vector of binary responses for cause 1.
`y.train2`	A vector of binary responses for cause 2.
`cond`	A vector of indices of `y.train` indicating censored subjects.
`binaryOffset`	The binary offset for `y.train`.
`binaryOffset2`	The binary offset for `y.train2`.
`tx.train`	A matrix with rows consisting of time and the covariates of the training data for cause 1.
`tx.train2`	A matrix with rows consisting of time and the covariates of the training data for cause 2.
`tx.test`	A matrix with rows consisting of time and the covariates of the test data, if any, for cause 1.
`tx.test2`	A matrix with rows consisting of time and the covariates of the test data, if any, for cause 2.

Examples


data(transplant)

delta <- (as.numeric(transplant$event)-1)

delta[delta==1] <- 4
delta[delta==2] <- 1
delta[delta>1] <- 2
table(delta, transplant$event)

table(1+floor(transplant$futime/30.5)) ## months
times <- 1+floor(transplant$futime/30.5)

typeO <- 1*(transplant$abo=='O')
typeA <- 1*(transplant$abo=='A')
typeB <- 1*(transplant$abo=='B')
typeAB <- 1*(transplant$abo=='AB')
table(typeA, typeO)

x.train <- cbind(typeO, typeA, typeB, typeAB)

N <- nrow(x.train)

x.test <- x.train

x.test[1:N, 1:4] <- matrix(c(1, 0, 0, 0), nrow=N, ncol=4, byrow=TRUE)

pre <- crisk.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

data(transplant)

delta <- (as.numeric(transplant$event)-1)

delta[delta==1] <- 4
delta[delta==2] <- 1
delta[delta>1] <- 2
table(delta, transplant$event)

table(1+floor(transplant$futime/30.5)) ## months
times <- 1+floor(transplant$futime/30.5)

typeO <- 1*(transplant$abo=='O')
typeA <- 1*(transplant$abo=='A')
typeB <- 1*(transplant$abo=='B')
typeAB <- 1*(transplant$abo=='AB')
table(typeA, typeO)

x.train <- cbind(typeO, typeA, typeB, typeAB)

N <- nrow(x.train)

x.test <- x.train

x.test[1:N, 1:4] <- matrix(c(1, 0, 0, 0), nrow=N, ncol=4, byrow=TRUE)

pre <- crisk.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

BART for competing risks

Description

Here we have implemented another approach to utilize BART for competing risks that is very flexible, and is akin to discrete-time survival analysis. Following the capabilities of BART, we allow for maximum flexibility in modeling the dependence of competing failure times on covariates. In particular, we do not impose proportional hazards.

Similar to crisk.bart, we utilize two BART models, yet they are two different BART models than previously considered. First, given an event of either cause occurred, we employ a typical binary BART model to discriminate between cause 1 and 2. Next, we proceed as if it were a typical survival analysis with BART for an absorbing event from either cause.

To elaborate, consider data in the form: $(s_i, \delta_i, {x}_i)$ where $s_i$ is the event time; $\delta_i$ is an indicator distinguishing events, $\delta_i=h$ due to cause $h in {1, 2}$ , from right-censoring, $\delta_i=0$ ; ${x}_i$ is a vector of covariates; and $i=1, ..., N$ indexes subjects. We denote the $K$ distinct event/censoring times by $0<t_{(1)}<...<t_{(K)}<\infty$ thus taking $t_{(j)}$ to be the $j^{th}$ order statistic among distinct observation times and, for convenience, $t_{(0)}=0$ .

First, consider event indicators for an event from either cause: $y_{1ij}$ for each subject $i$ at each distinct time $t_{(j)}$ up to and including the subject's last observation time $s_i=t_{(n_i)}$ with $n_i=\arg \max_j [t_{(j)}\leq s_i]$ . We denote by $p_{1ij}$ the probability of an event at time $t_{(j)}$ conditional on no previous event. We now write the model for $y_{1ij}$ as a nonparametric probit (or logistic) regression of $y_{1ij}$ on the time $t_{(j)}$ and the covariates ${x}_{1i}$ , and then utilize BART for binary responses. Specifically, $y_{1ij}\ =\ I[\delta_i>0] I[s_i=t_{(j)}],\ j=1, ..., n_i$ . Therefore, we have $p_{1ij} = F(mu_{1ij}),\ mu_{1ij} = mu_1+f_1(t_{(j)}, {x}_{1i})$ where $F$ denotes the Normal (or Logistic) cdf.

Next, we denote by $p_{2i}$ the probability of a cause 1 event at time $s_i$ conditional on an event having occurred. We now write the model for $y_{2i}$ as a nonparametric probit (or logistic) regression of $y_{2i}$ on the time $s_i$ and the covariates ${x}_{2i}$ , via BART for binary responses. Specifically, $y_{2i}\ =\ I[\delta_i=1]$ . Therefore, we have $p_{2i} = F(mu_{2i}),\ mu_{2i} = mu_2+f_2(s_i, {x}_{2i})$ where $F$ denotes the Normal (or Logistic) cdf. Although, we modeled $p_{2i}$ at the time of an event, $s_i$ , we can estimate this probability at any other time points on the grid via $p(t_{(j)}, x_2)=F( mu_2+f_2(t_{(j)}, {x}_2))$ . Finally, based on these probabilities, $p_{hij}$ , we can construct targets of inference such as the cumulative incidence functions.

Usage


crisk2.bart(x.train=matrix(0,0,0), y.train=NULL,
           x.train2=x.train, y.train2=NULL,
           times=NULL, delta=NULL, K=NULL,
           x.test=matrix(0,0,0), x.test2=x.test, 
           sparse=FALSE, theta=0, omega=1,
           a=0.5, b=1, augment=FALSE,
           rho=NULL, rho2=NULL,
           xinfo=matrix(0,0,0), xinfo2=matrix(0,0,0),
           usequants=FALSE, 
           rm.const=TRUE, type='pbart',
           ntype=as.integer(
               factor(type, levels=c('wbart', 'pbart', 'lbart'))),
           k=2, power=2, base=0.95,
           offset=NULL, offset2=NULL,
           tau.num=c(NA, 3, 6)[ntype],
           
           ntree=50, numcut=100, ndpost=1000, nskip=250,
           keepevery = 10L,
           
           
           
           
           printevery=100L, 
           
           id=NULL,    ## crisk2.bart only
           seed=99,    ## mc.crisk2.bart only
           mc.cores=2, ## mc.crisk2.bart only
           nice=19L    ## mc.crisk2.bart only
          )

mc.crisk2.bart(x.train=matrix(0,0,0), y.train=NULL,
              x.train2=x.train, y.train2=NULL,
              times=NULL, delta=NULL, K=NULL,
              x.test=matrix(0,0,0), x.test2=x.test, 
              sparse=FALSE, theta=0, omega=1,
              a=0.5, b=1, augment=FALSE,
              rho=NULL, rho2=NULL,
              xinfo=matrix(0,0,0), xinfo2=matrix(0,0,0),
              usequants=FALSE, 
              rm.const=TRUE, type='pbart',
              ntype=as.integer(
                  factor(type, levels=c('wbart', 'pbart', 'lbart'))),
              k=2, power=2, base=0.95,
              offset=NULL, offset2=NULL,
              tau.num=c(NA, 3, 6)[ntype],
              
              ntree=50, numcut=100, ndpost=1000, nskip=250,
              keepevery = 10L,
              
              
              
              printevery=100L, 
              
              id=NULL,    ## crisk2.bart only
              seed=99,    ## mc.crisk2.bart only
              mc.cores=2, ## mc.crisk2.bart only
              nice=19L    ## mc.crisk2.bart only
             )
crisk2.bart(x.train=matrix(0,0,0), y.train=NULL,
           x.train2=x.train, y.train2=NULL,
           times=NULL, delta=NULL, K=NULL,
           x.test=matrix(0,0,0), x.test2=x.test, 
           sparse=FALSE, theta=0, omega=1,
           a=0.5, b=1, augment=FALSE,
           rho=NULL, rho2=NULL,
           xinfo=matrix(0,0,0), xinfo2=matrix(0,0,0),
           usequants=FALSE, 
           rm.const=TRUE, type='pbart',
           ntype=as.integer(
               factor(type, levels=c('wbart', 'pbart', 'lbart'))),
           k=2, power=2, base=0.95,
           offset=NULL, offset2=NULL,
           tau.num=c(NA, 3, 6)[ntype],
           
           ntree=50, numcut=100, ndpost=1000, nskip=250,
           keepevery = 10L,
           
           
           
           
           printevery=100L, 
           
           id=NULL,    ## crisk2.bart only
           seed=99,    ## mc.crisk2.bart only
           mc.cores=2, ## mc.crisk2.bart only
           nice=19L    ## mc.crisk2.bart only
          )

mc.crisk2.bart(x.train=matrix(0,0,0), y.train=NULL,
              x.train2=x.train, y.train2=NULL,
              times=NULL, delta=NULL, K=NULL,
              x.test=matrix(0,0,0), x.test2=x.test, 
              sparse=FALSE, theta=0, omega=1,
              a=0.5, b=1, augment=FALSE,
              rho=NULL, rho2=NULL,
              xinfo=matrix(0,0,0), xinfo2=matrix(0,0,0),
              usequants=FALSE, 
              rm.const=TRUE, type='pbart',
              ntype=as.integer(
                  factor(type, levels=c('wbart', 'pbart', 'lbart'))),
              k=2, power=2, base=0.95,
              offset=NULL, offset2=NULL,
              tau.num=c(NA, 3, 6)[ntype],
              
              ntree=50, numcut=100, ndpost=1000, nskip=250,
              keepevery = 10L,
              
              
              
              printevery=100L, 
              
              id=NULL,    ## crisk2.bart only
              seed=99,    ## mc.crisk2.bart only
              mc.cores=2, ## mc.crisk2.bart only
              nice=19L    ## mc.crisk2.bart only
             )

Arguments

`x.train`	Covariates for training (in sample) data for an event. Must be a data.frame or a matrix with rows corresponding to observations and columns to variables. `crisk2.bart` will generate draws of $f_1(t, x)$ for each $x$ which is a row of `x.train` (note that the definition of `x.train` is dependent on whether `y.train` has been specified; see below).
`y.train`	Event binary response for training (in sample) data. If `y.train` is `NULL`, then `y.train` (`x.train` and `x.test`, if specified) are generated by a call to `surv.pre.bart` (which require that `times` and `delta` be provided: see below); otherwise, `y.train` (`x.train` and `x.test`, if specified) are utilized as given assuming that the data construction has already been performed.
`x.train2`	Covariates for training (in sample) data of for a cause 1 event. Similar to `x.train` above.
`y.train2`	Cause 1 event binary response for training (in sample) data. Similar to `y.train` above.
`times`	The time of event or right-censoring, $s_i$ . If `y.train` is `NULL`, then `times` (and `delta`) must be provided.
`delta`	The event indicator: 1 for cause 1, 2 for cause 2 and 0 is censored. If `y.train` is `NULL`, then `delta` (and `times`) must be provided.
`K`	If provided, then coarsen `times` per the quantiles $1/K, 2/K, ..., K/K$ .
`x.test`	Covariates for test (out of sample) data of an event. Must be a data.frame or a matrix and have the same structure as `x.train`. `crisk2.bart` will generate draws of $f_1(t, x)$ for each $x$ which is a row of `x.test`.
`x.test2`	Covariates for test (out of sample) data of a cause 1 event. Similar to `x.test` above.
`sparse`	Whether to perform variable selection based on a sparse Dirichlet prior; see Linero 2016.
`theta`	Set $theta$ parameter; zero means random.
`omega`	Set $omega$ parameter; zero means random.
`a`	Sparse parameter for $Beta(a, b)$ prior: $0.5<=a<=1$ where lower values inducing more sparsity.
`b`	Sparse parameter for $Beta(a, b)$ prior; typically, `b=1`.
`rho`	Sparse parameter: typically `rho=p` where `p` is the number of covariates in `x.train`.
`rho2`	Sparse parameter: typically `rho2=p` where `p` is the number of covariates in `x.train2`.
`augment`	Whether data augmentation is to be performed in sparse variable selection.
`xinfo`	You can provide the cutpoints to BART or let BART choose them for you. To provide them, use the `xinfo` argument to specify a list (matrix) where the items (rows) are the covariates and the contents of the items (columns) are the cutpoints.
`xinfo2`	Cause 2 cutpoints.
`usequants`	If `usequants=FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, uniform quantiles are used for the cutpoints.
`rm.const`	Whether or not to remove constant variables.
`type`	Whether to employ probit BART via Albert-Chib, `'pbart'`, or logistic BART by Holmes-Held, `'lbart'`.
`ntype`	The integer equivalent of `type` where `'wbart'` is 1, `'pbart'` is 2 and `'lbart'` is 3.
`k`	k is the number of prior standard deviations $f_h(t, x)$ is away from +/-3. The bigger k is, the more conservative the fitting will be.
`power`	Power parameter for tree prior.
`base`	Base parameter for tree prior.
`offset`	Cause 1 binary offset.
`offset2`	Cause 2 binary offset.
`tau.num`	The numerator in the `tau` definition.
`ntree`	The number of trees in the sum.
`numcut`	The number of possible values of cutpoints (see `usequants`). If a single number if given, this is used for all variables. Otherwise a vector with length equal to `ncol(x.train)` is required, where the $i^{th}$ element gives the number of cutpoints used for the $i^{th}$ variable in `x.train`. If `usequants` is `FALSE`, `numcut` equally spaced cutoffs are used covering the range of values in the corresponding column of `x.train`. If `usequants` is `TRUE`, then `min(numcut, the number of unique values in the corresponding columns of x.train - 1)` cutpoint values are used.
`ndpost`	The number of posterior draws returned.
`nskip`	Number of MCMC iterations to be treated as burn in.
`keepevery`	Every `keepevery` draw is kept to be returned to the user.
`printevery`	As the MCMC runs, a message is printed every `printevery` draws.
`id`	`crisk2.bart` only: unique identifier added to returned list.
`seed`	`mc.crisk2.bart` only: seed required for reproducible MCMC.
`mc.cores`	`mc.crisk2.bart` only: number of cores to employ in parallel.
`nice`	`mc.crisk2.bart` only: set the job niceness. The default niceness is 19: niceness goes from 0 (highest priority) to 19 (lowest priority).

Value

crisk2.bart returns an object of type crisk2bart which is essentially a list. Besides the items listed below, the list has offset, offset2, times which are the unique times, K which is the number of unique times, tx.train and tx.test, if any.

`yhat.train`	A matrix with `ndpost` rows and `nrow(x.train)` columns. Each row corresponds to a draw $f^_1$ from the posterior of $f_1$ and each column corresponds to a row of `x.train`. The $(i,j)$ value is $f^_1(t, x)$ for the $i^{th}$ kept draw of $f_1$ and the $j^{th}$ row of `x.train`. Burn-in is dropped.
`yhat.test`	Same as `yhat.train` but now the x's are the rows of the test data.
`surv.test`	test data fits for the survival function, $S(t, x)$ .
`surv.test.mean`	mean of `surv.test` over the posterior samples.
`prob.test`	The probability of suffering an event.
`prob.test2`	The probability of suffering a cause 1 event.
`cif.test`	The cumulative incidence function of cause 1, $F_1(t, x)$ .
`cif.test2`	The cumulative incidence function of cause 2, $F_2(t, x)$ .
`cif.test.mean`	mean of `cif.test` columns for cause 1.
`cif.test2.mean`	mean of `cif.test2` columns for cause 2.
`varcount`	a matrix with `ndpost` rows and `nrow(x.train)` columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times this variable is used for an event in a tree decision rule (over all trees) is given.
`varcount2`	For each variable the total count of the number of times this variable is used for a cause 1 event in a tree decision rule is given.

Examples


data(transplant)

pfit <- survfit(Surv(futime, event) ~ abo, transplant)

# competing risks for type O
plot(pfit[4,], xscale=7, xmax=735, col=1:3, lwd=2, ylim=c(0, 1),
       xlab='t (weeks)', ylab='Aalen-Johansen (AJ) CI(t)')
    legend(450, .4, c("Death", "Transplant", "Withdrawal"), col=1:3, lwd=2)
## plot(pfit[4,], xscale=30.5, xmax=735, col=1:3, lwd=2, ylim=c(0, 1),
##        xlab='t (months)', ylab='Aalen-Johansen (AJ) CI(t)')
##     legend(450, .4, c("Death", "Transplant", "Withdrawal"), col=1:3, lwd=2)

delta <- (as.numeric(transplant$event)-1)
## recode so that delta=1 is cause of interest; delta=2 otherwise
delta[delta==1] <- 4
delta[delta==2] <- 1
delta[delta>1] <- 2
table(delta, transplant$event)

times <- pmax(1, ceiling(transplant$futime/7)) ## weeks
##times <- pmax(1, ceiling(transplant$futime/30.5)) ## months
table(times)

typeO <- 1*(transplant$abo=='O')
typeA <- 1*(transplant$abo=='A')
typeB <- 1*(transplant$abo=='B')
typeAB <- 1*(transplant$abo=='AB')
table(typeA, typeO)

x.train <- cbind(typeO, typeA, typeB, typeAB)

x.test <- cbind(1, 0, 0, 0)
dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

##test BART with token run to ensure installation works
set.seed(99)
post <- crisk2.bart(x.train=x.train, times=times, delta=delta,
                   x.test=x.test, nskip=1, ndpost=1, keepevery=1)

## Not run: 

## run one long MCMC chain in one process
## set.seed(99)
## post <- crisk2.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

## in the interest of time, consider speeding it up by parallel processing
## run "mc.cores" number of shorter MCMC chains in parallel processes
post <- mc.crisk2.bart(x.train=x.train, times=times, delta=delta,
                      x.test=x.test, seed=99, mc.cores=8)

K <- post$K

typeO.cif.mean <- apply(post$cif.test, 2, mean)
typeO.cif.025 <- apply(post$cif.test, 2, quantile, probs=0.025)
typeO.cif.975 <- apply(post$cif.test, 2, quantile, probs=0.975)

plot(pfit[4,], xscale=7, xmax=735, col=1:3, lwd=2, ylim=c(0, 0.8),
       xlab='t (weeks)', ylab='CI(t)')
points(c(0, post$times)*7, c(0, typeO.cif.mean), col=4, type='s', lwd=2)
points(c(0, post$times)*7, c(0, typeO.cif.025), col=4, type='s', lwd=2, lty=2)
points(c(0, post$times)*7, c(0, typeO.cif.975), col=4, type='s', lwd=2, lty=2)
     legend(450, .4, c("Transplant(BART)", "Transplant(AJ)",
                       "Death(AJ)", "Withdrawal(AJ)"),
            col=c(4, 2, 1, 3), lwd=2)
##dev.copy2pdf(file='../vignettes/figures/liver-BART.pdf')
## plot(pfit[4,], xscale=30.5, xmax=735, col=1:3, lwd=2, ylim=c(0, 0.8),
##        xlab='t (months)', ylab='CI(t)')
## points(c(0, post$times)*30.5, c(0, typeO.cif.mean), col=4, type='s', lwd=2)
## points(c(0, post$times)*30.5, c(0, typeO.cif.025), col=4, type='s', lwd=2, lty=2)
## points(c(0, post$times)*30.5, c(0, typeO.cif.975), col=4, type='s', lwd=2, lty=2)
##      legend(450, .4, c("Transplant(BART)", "Transplant(AJ)",
##                        "Death(AJ)", "Withdrawal(AJ)"),
##             col=c(4, 2, 1, 3), lwd=2)


## End(Not run)
data(transplant)

pfit <- survfit(Surv(futime, event) ~ abo, transplant)

# competing risks for type O
plot(pfit[4,], xscale=7, xmax=735, col=1:3, lwd=2, ylim=c(0, 1),
       xlab='t (weeks)', ylab='Aalen-Johansen (AJ) CI(t)')
    legend(450, .4, c("Death", "Transplant", "Withdrawal"), col=1:3, lwd=2)
## plot(pfit[4,], xscale=30.5, xmax=735, col=1:3, lwd=2, ylim=c(0, 1),
##        xlab='t (months)', ylab='Aalen-Johansen (AJ) CI(t)')
##     legend(450, .4, c("Death", "Transplant", "Withdrawal"), col=1:3, lwd=2)

delta <- (as.numeric(transplant$event)-1)
## recode so that delta=1 is cause of interest; delta=2 otherwise
delta[delta==1] <- 4
delta[delta==2] <- 1
delta[delta>1] <- 2
table(delta, transplant$event)

times <- pmax(1, ceiling(transplant$futime/7)) ## weeks
##times <- pmax(1, ceiling(transplant$futime/30.5)) ## months
table(times)

typeO <- 1*(transplant$abo=='O')
typeA <- 1*(transplant$abo=='A')
typeB <- 1*(transplant$abo=='B')
typeAB <- 1*(transplant$abo=='AB')
table(typeA, typeO)

x.train <- cbind(typeO, typeA, typeB, typeAB)

x.test <- cbind(1, 0, 0, 0)
dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

##test BART with token run to ensure installation works
set.seed(99)
post <- crisk2.bart(x.train=x.train, times=times, delta=delta,
                   x.test=x.test, nskip=1, ndpost=1, keepevery=1)

## Not run: 

## run one long MCMC chain in one process
## set.seed(99)
## post <- crisk2.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

## in the interest of time, consider speeding it up by parallel processing
## run "mc.cores" number of shorter MCMC chains in parallel processes
post <- mc.crisk2.bart(x.train=x.train, times=times, delta=delta,
                      x.test=x.test, seed=99, mc.cores=8)

K <- post$K

typeO.cif.mean <- apply(post$cif.test, 2, mean)
typeO.cif.025 <- apply(post$cif.test, 2, quantile, probs=0.025)
typeO.cif.975 <- apply(post$cif.test, 2, quantile, probs=0.975)

plot(pfit[4,], xscale=7, xmax=735, col=1:3, lwd=2, ylim=c(0, 0.8),
       xlab='t (weeks)', ylab='CI(t)')
points(c(0, post$times)*7, c(0, typeO.cif.mean), col=4, type='s', lwd=2)
points(c(0, post$times)*7, c(0, typeO.cif.025), col=4, type='s', lwd=2, lty=2)
points(c(0, post$times)*7, c(0, typeO.cif.975), col=4, type='s', lwd=2, lty=2)
     legend(450, .4, c("Transplant(BART)", "Transplant(AJ)",
                       "Death(AJ)", "Withdrawal(AJ)"),
            col=c(4, 2, 1, 3), lwd=2)
##dev.copy2pdf(file='../vignettes/figures/liver-BART.pdf')
## plot(pfit[4,], xscale=30.5, xmax=735, col=1:3, lwd=2, ylim=c(0, 0.8),
##        xlab='t (months)', ylab='CI(t)')
## points(c(0, post$times)*30.5, c(0, typeO.cif.mean), col=4, type='s', lwd=2)
## points(c(0, post$times)*30.5, c(0, typeO.cif.025), col=4, type='s', lwd=2, lty=2)
## points(c(0, post$times)*30.5, c(0, typeO.cif.975), col=4, type='s', lwd=2, lty=2)
##      legend(450, .4, c("Transplant(BART)", "Transplant(AJ)",
##                        "Death(AJ)", "Withdrawal(AJ)"),
##             col=c(4, 2, 1, 3), lwd=2)


## End(Not run)

Testing truncated Normal sampling

Description

Truncated Normal latents with non-unit variance are necessary for logistic BART.

Usage

draw_lambda_i(lambda, mean, kmax=1000, thin=1)
draw_lambda_i(lambda, mean, kmax=1000, thin=1)

Arguments

`lambda`	Previous value of lambda.
`mean`	Mean of truncated Normal.
`kmax`	The number of terms in the mixture.
`thin`	The thinning parameter.

Value

Returns the variance for a truncated Normal, i.e., $N(mean, lambda)I(tau, infinity)$ .

Examples


set.seed(12)

draw_lambda_i(1, 2)
rtnorm(1, 2, sqrt(6.773462), 6)
draw_lambda_i(6.773462, 2)

set.seed(12)

draw_lambda_i(1, 2)
rtnorm(1, 2, sqrt(6.773462), 6)
draw_lambda_i(6.773462, 2)

Generalized BART for continuous and binary outcomes

Description

BART is a Bayesian “sum-of-trees” model.
For a numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .

$f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

gbart(
      x.train, y.train,
      x.test=matrix(0,0,0), type='wbart',
      ntype=as.integer(
          factor(type, levels=c('wbart', 'pbart', 'lbart'))),
      sparse=FALSE, theta=0, omega=1,
      a=0.5, b=1, augment=FALSE, rho=NULL,
      xinfo=matrix(0,0,0), usequants=FALSE,
      rm.const=TRUE,
      sigest=NA, sigdf=3, sigquant=0.90,
      k=2, power=2, base=0.95,
      
      lambda=NA, tau.num=c(NA, 3, 6)[ntype], 
      offset=NULL, w=rep(1, length(y.train)),
      ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
      
      ndpost=1000L, nskip=100L, 
      keepevery=c(1L, 10L, 10L)[ntype],
      printevery=100L, transposed=FALSE,
      hostname=FALSE,
      mc.cores = 1L, ## mc.gbart only
      nice = 19L,    ## mc.gbart only
      seed = 99L     ## mc.gbart only
)

mc.gbart(
         x.train, y.train,
         x.test=matrix(0,0,0), type='wbart',
         ntype=as.integer(
             factor(type, levels=c('wbart', 'pbart', 'lbart'))),
         sparse=FALSE, theta=0, omega=1,
         a=0.5, b=1, augment=FALSE, rho=NULL,
         xinfo=matrix(0,0,0), usequants=FALSE,
         rm.const=TRUE,
         sigest=NA, sigdf=3, sigquant=0.90,
         k=2, power=2, base=0.95,
         
         lambda=NA, tau.num=c(NA, 3, 6)[ntype], 
         offset=NULL, w=rep(1, length(y.train)),
         
         ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
         ndpost=1000L, nskip=100L, 
         keepevery=c(1L, 10L, 10L)[ntype],
         printevery=100L, transposed=FALSE,
         hostname=FALSE,
         mc.cores = 2L, nice = 19L, seed = 99L
)

gbart(
      x.train, y.train,
      x.test=matrix(0,0,0), type='wbart',
      ntype=as.integer(
          factor(type, levels=c('wbart', 'pbart', 'lbart'))),
      sparse=FALSE, theta=0, omega=1,
      a=0.5, b=1, augment=FALSE, rho=NULL,
      xinfo=matrix(0,0,0), usequants=FALSE,
      rm.const=TRUE,
      sigest=NA, sigdf=3, sigquant=0.90,
      k=2, power=2, base=0.95,
      
      lambda=NA, tau.num=c(NA, 3, 6)[ntype], 
      offset=NULL, w=rep(1, length(y.train)),
      ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
      
      ndpost=1000L, nskip=100L, 
      keepevery=c(1L, 10L, 10L)[ntype],
      printevery=100L, transposed=FALSE,
      hostname=FALSE,
      mc.cores = 1L, ## mc.gbart only
      nice = 19L,    ## mc.gbart only
      seed = 99L     ## mc.gbart only
)

mc.gbart(
         x.train, y.train,
         x.test=matrix(0,0,0), type='wbart',
         ntype=as.integer(
             factor(type, levels=c('wbart', 'pbart', 'lbart'))),
         sparse=FALSE, theta=0, omega=1,
         a=0.5, b=1, augment=FALSE, rho=NULL,
         xinfo=matrix(0,0,0), usequants=FALSE,
         rm.const=TRUE,
         sigest=NA, sigdf=3, sigquant=0.90,
         k=2, power=2, base=0.95,
         
         lambda=NA, tau.num=c(NA, 3, 6)[ntype], 
         offset=NULL, w=rep(1, length(y.train)),
         
         ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
         ndpost=1000L, nskip=100L, 
         keepevery=c(1L, 10L, 10L)[ntype],
         printevery=100L, transposed=FALSE,
         hostname=FALSE,
         mc.cores = 2L, nice = 19L, seed = 99L
)

Arguments

`x.train`	Explanatory variables for training (in sample) data. May be a matrix or a data frame, with (as usual) rows corresponding to observations and columns to variables. If a variable is a factor in a data frame, it is replaced with dummies. Note that $q$ dummies are created if $q>2$ and one dummy created if $q=2$ where $q$ is the number of levels of the factor. `gbart` will generate draws of $f(x)$ for each $x$ which is a row of `x.train`.
`y.train`	Continuous or binary dependent variable for training (in sample) data. If $y$ is numeric, then a continuous BART model is fit (Normal errors). If $y$ is binary (has only 0's and 1's), then a binary BART model with a probit link is fit by default: you can over-ride the default via the argument `type` to specify a logit BART model.
`x.test`	Explanatory variables for test (out of sample) data. Should have same structure as `x.train`. `gbart` will generate draws of $f(x)$ for each $x$ which is a row of `x.test`.
`type`	You can use this argument to specify the type of fit. `'wbart'` for continuous BART, `'pbart'` for probit BART or `'lbart'` for logit BART.
`ntype`	The integer equivalent of `type` where `'wbart'` is 1, `'pbart'` is 2 and `'lbart'` is 3.
`sparse`	Whether to perform variable selection based on a sparse Dirichlet prior rather than simply uniform; see Linero 2016.
`theta`	Set $theta$ parameter; zero means random.
`omega`	Set $omega$ parameter; zero means random.
`a`	Sparse parameter for $Beta(a, b)$ prior: $0.5<=a<=1$ where lower values inducing more sparsity.
`b`	Sparse parameter for $Beta(a, b)$ prior; typically, $b=1$ .
`rho`	Sparse parameter: typically $rho=p$ where $p$ is the number of covariates under consideration.
`augment`	Whether data augmentation is to be performed in sparse variable selection.
`xinfo`	You can provide the cutpoints to BART or let BART choose them for you. To provide them, use the `xinfo` argument to specify a list (matrix) where the items (rows) are the covariates and the contents of the items (columns) are the cutpoints.
`usequants`	If `usequants=FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, uniform quantiles are used for the cutpoints.
`rm.const`	Whether or not to remove constant variables.
`sigest`	The prior for the error variance ( $sigma^2$ ) is inverted chi-squared (the standard conditionally conjugate prior). The prior is specified by choosing the degrees of freedom, a rough estimate of the corresponding standard deviation and a quantile to put this rough estimate at. If `sigest=NA` then the rough estimate will be the usual least squares estimator. Otherwise the supplied value will be used. Not used if $y$ is binary.
`sigdf`	Degrees of freedom for error variance prior. Not used if $y$ is binary.
`sigquant`	The quantile of the prior that the rough estimate (see `sigest`) is placed at. The closer the quantile is to 1, the more aggresive the fit will be as you are putting more prior weight on error standard deviations ( $sigma$ ) less than the rough estimate. Not used if $y$ is binary.
`k`	For numeric $y$ , `k` is the number of prior standard deviations $E(Y\|x) = f(x)$ is away from +/-0.5. For binary $y$ , `k` is the number of prior standard deviations $f(x)$ is away from +/-3. The bigger `k` is, the more conservative the fitting will be.
`power`	Power parameter for tree prior.
`base`	Base parameter for tree prior.
`lambda`	The scale of the prior for the variance. If `lambda` is zero, then the variance is to be considered fixed and known at the given value of `sigest`. Not used if $y$ is binary.
`tau.num`	The numerator in the `tau` definition, i.e., `tau=tau.num/(k*sqrt(ntree))`.
`offset`	Continous BART operates on `y.train` centered by `offset` which defaults to `mean(y.train)`. With binary BART, the centering is $P(Y=1 \| x) = F(f(x) + offset)$ where `offset` defaults to `F^{-1}(mean(y.train))`. You can use the `offset` parameter to over-ride these defaults.
`w`	Vector of weights which multiply the standard deviation. Not used if $y$ is binary.
`ntree`	The number of trees in the sum.
`numcut`	The number of possible values of $c$ (see `usequants`). If a single number if given, this is used for all variables. Otherwise a vector with length equal to `ncol(x.train)` is required, where the $i^{th}$ element gives the number of $c$ used for the $i^{th}$ variable in `x.train`. If usequants is false, numcut equally spaced cutoffs are used covering the range of values in the corresponding column of `x.train`. If `usequants` is true, then $min(numcut, the number of unique values in the corresponding columns of x.train - 1)$ values are used.
`ndpost`	The number of posterior draws returned.
`nskip`	Number of MCMC iterations to be treated as burn in.
`printevery`	As the MCMC runs, a message is printed every printevery draws.
`keepevery`	Every keepevery draw is kept to be returned to the user.
`transposed`	When running `gbart` in parallel, it is more memory-efficient to transpose `x.train` and `x.test`, if any, prior to calling `mc.gbart`.
`hostname`	When running on a cluster occasionally it is useful to track on which node each chain is running; to do so set this argument to `TRUE`.
`seed`	Setting the seed required for reproducible MCMC.
`mc.cores`	Number of cores to employ in parallel.
`nice`	Set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest).

Details

BART is a Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

For x.train/x.test with missing data elements, gbart will singly impute them with hot decking. For one or more missing covariates, record-level hot-decking imputation deWaPann11 is employed that is biased towards the null, i.e., nonmissing values from another record are randomly selected regardless of the outcome. Since mc.gbart runs multiple gbart threads in parallel, mc.gbart performs multiple imputation with hot decking, i.e., a separate imputation for each thread. This record-level hot-decking imputation is biased towards the null, i.e., nonmissing values from another record are randomly selected regardless of y.train.

Value

gbart returns an object of type gbart which is essentially a list. In the numeric $y$ case, the list has components:

`yhat.train`	A matrix with ndpost rows and nrow(x.train) columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to a row of x.train. The $(i,j)$ value is $f^(x)$ for the $i^{th}$ kept draw of $f$ and the $j^{th}$ row of x.train. Burn-in is dropped.
`yhat.test`	Same as yhat.train but now the x's are the rows of the test data.
`yhat.train.mean`	train data fits = mean of yhat.train columns.
`yhat.test.mean`	test data fits = mean of yhat.test columns.
`sigma`	post burn in draws of sigma, length = ndpost.
`first.sigma`	burn-in draws of sigma.
`varcount`	a matrix with ndpost rows and nrow(x.train) columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times that variable is used in a tree decision rule (over all trees) is given.
`sigest`	The rough error standard deviation ( $\sigma$ ) used in the prior.

Examples

##simulate data (example from Friedman MARS paper)
f = function(x){
10*sin(pi*x[,1]*x[,2]) + 20*(x[,3]-.5)^2+10*x[,4]+5*x[,5]
}
sigma = 1.0  #y = f(x) + sigma*z , z~N(0,1)
n = 100      #number of observations
set.seed(99)
x=matrix(runif(n*10),n,10) #10 variables, only first 5 matter
Ey = f(x)
y=Ey+sigma*rnorm(n)
lmFit = lm(y~.,data.frame(x,y)) #compare lm fit to BART later

##test BART with token run to ensure installation works
set.seed(99)
bartFit = wbart(x,y,nskip=5,ndpost=5)

## Not run: 
##run BART
set.seed(99)
bartFit = wbart(x,y)

##compare BART fit to linear matter and truth = Ey
fitmat = cbind(y,Ey,lmFit$fitted,bartFit$yhat.train.mean)
colnames(fitmat) = c('y','Ey','lm','bart')
print(cor(fitmat))

## End(Not run)
##simulate data (example from Friedman MARS paper)
f = function(x){
10*sin(pi*x[,1]*x[,2]) + 20*(x[,3]-.5)^2+10*x[,4]+5*x[,5]
}
sigma = 1.0  #y = f(x) + sigma*z , z~N(0,1)
n = 100      #number of observations
set.seed(99)
x=matrix(runif(n*10),n,10) #10 variables, only first 5 matter
Ey = f(x)
y=Ey+sigma*rnorm(n)
lmFit = lm(y~.,data.frame(x,y)) #compare lm fit to BART later

##test BART with token run to ensure installation works
set.seed(99)
bartFit = wbart(x,y,nskip=5,ndpost=5)

## Not run: 
##run BART
set.seed(99)
bartFit = wbart(x,y)

##compare BART fit to linear matter and truth = Ey
fitmat = cbind(y,Ey,lmFit$fitted,bartFit$yhat.train.mean)
colnames(fitmat) = c('y','Ey','lm','bart')
print(cor(fitmat))

## End(Not run)

Geweke's convergence diagnostic

Description

Geweke (1992) proposed a convergence diagnostic for Markov chains based on a test for equality of the means of the first and last part of a Markov chain (by default the first 10% and the last 50%). If the samples are drawn from the stationary distribution of the chain, the two means are equal and Geweke's statistic has an asymptotically standard normal distribution.

The test statistic is a standard Z-score: the difference between the two sample means divided by its estimated standard error. The standard error is estimated from the spectral density at zero and so takes into account any autocorrelation.

The Z-score is calculated under the assumption that the two parts of the chain are asymptotically independent, which requires that the sum of frac1 and frac2 be strictly less than 1.

Adapted from the geweke.diag function of the coda package which passes mcmc objects as arguments rather than matrices.

Usage

gewekediag(x, frac1=0.1, frac2=0.5)gewekediag(x, frac1=0.1, frac2=0.5)

Arguments

`x`	Matrix of MCMC chains: the rows are the samples and the columns are different "parameters". For BART, generally, the columns are estimates of $f$ . For `pbart`, they are different subjects. For `surv.bart`, they are different subjects at a grid of times.
`frac1`	fraction to use from beginning of chain
`frac2`	fraction to use from end of chain

Value

Z-scores for a test of equality of means between the first and last parts of the chain. A separate statistic is calculated for each variable in each chain.

References

Geweke J. (1992) Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments. In JM Bernado, JO Berger, AP Dawid, AFM Smith (eds.), Bayesian Statistics 4, pp. 169-193. Oxford University Press, Oxford.

Plummer M., Best N., Cowles K. and Vines K. (2006) CODA: Convergence Diagnosis and Output Analysis for MCMC. R News, vol 6, 7-11.

Examples


## load survival package for the advanced lung cancer example
data(lung)

group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno
times <- lung[group, 2]   ##lung$time
delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead
                          ##delta: 0=censored, 1=dead

## this study reports time in days rather than months like other studies
## coarsening from days to months will reduce the computational burden
times <- ceiling(times/30)

summary(times)
table(delta)

x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates

## lung$age:        Age in years
## lung$sex:        Male=1 Female=2
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)')

summary(x.train[ , 1])
table(x.train[ , 2])
table(x.train[ , 3])

x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios

dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

i <- 1

for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) {
    x.test[i, ] <- c(age, sex, ph.karno)
    i <- i+1
}

## Not run: 
    set.seed(99)
    post <- surv.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)
    ## in the interest of time, consider speeding it up by parallel processing
    ## run "mc.cores" number of shorter MCMC chains in parallel processes
    ## post <- mc.surv.bart(x.train=x.train, times=times, delta=delta,
    ##                      x.test=x.test, mc.cores=8, seed=99)

    N <- nrow(x.test)

    K <- post$K
    ## select 10 lung cancer patients uniformly spread out over the data set
    h <- seq(1, N*K, floor(N/10)*K)

    for(i in h) {
        post.mcmc <- post$yhat.test[ , (i-1)+1:K]
        z <- gewekediag(post.mcmc)$z
        y <- max(c(4, abs(z)))

        ## plot the z scores vs. time for each patient
        if(i==1) plot(post$times, z, ylim=c(-y, y), type='l',
                      xlab='t', ylab='z')
        else lines(post$times, z, type='l')
    }
    ## add two-sided alpha=0.05 critical value lines
    lines(post$times, rep(-1.96, K), type='l', lty=2)
    lines(post$times, rep( 1.96, K), type='l', lty=2)


## End(Not run)

## load survival package for the advanced lung cancer example
data(lung)

group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno
times <- lung[group, 2]   ##lung$time
delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead
                          ##delta: 0=censored, 1=dead

## this study reports time in days rather than months like other studies
## coarsening from days to months will reduce the computational burden
times <- ceiling(times/30)

summary(times)
table(delta)

x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates

## lung$age:        Age in years
## lung$sex:        Male=1 Female=2
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)')

summary(x.train[ , 1])
table(x.train[ , 2])
table(x.train[ , 3])

x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios

dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

i <- 1

for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) {
    x.test[i, ] <- c(age, sex, ph.karno)
    i <- i+1
}

## Not run: 
    set.seed(99)
    post <- surv.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)
    ## in the interest of time, consider speeding it up by parallel processing
    ## run "mc.cores" number of shorter MCMC chains in parallel processes
    ## post <- mc.surv.bart(x.train=x.train, times=times, delta=delta,
    ##                      x.test=x.test, mc.cores=8, seed=99)

    N <- nrow(x.test)

    K <- post$K
    ## select 10 lung cancer patients uniformly spread out over the data set
    h <- seq(1, N*K, floor(N/10)*K)

    for(i in h) {
        post.mcmc <- post$yhat.test[ , (i-1)+1:K]
        z <- gewekediag(post.mcmc)$z
        y <- max(c(4, abs(z)))

        ## plot the z scores vs. time for each patient
        if(i==1) plot(post$times, z, ylim=c(-y, y), type='l',
                      xlab='t', ylab='z')
        else lines(post$times, z, type='l')
    }
    ## add two-sided alpha=0.05 critical value lines
    lines(post$times, rep(-1.96, K), type='l', lty=2)
    lines(post$times, rep( 1.96, K), type='l', lty=2)


## End(Not run)

Logit BART for dichotomous outcomes with Logistic latents

Description

BART is a Bayesian “sum-of-trees” model.
For numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim Log(0, 1)$ .
For a binary response $y$ , $P(Y=1 | x) = F(f(x))$ , where $F$ denotes the standard Logistic CDF (logit link).

In both cases, $f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

lbart(
   x.train, y.train, x.test=matrix(0.0,0,0),
   sparse=FALSE, a=0.5, b=1, augment=FALSE, rho=NULL,
   xinfo=matrix(0.0,0,0), usequants=FALSE,
   cont=FALSE, rm.const=TRUE, tau.interval=0.95,
   k=2.0, power=2.0, base=.95, 
   binaryOffset=NULL,
   ntree=200L, numcut=100L,
   ndpost=1000L, nskip=100L,
   keepevery=1L,
   nkeeptrain=ndpost, nkeeptest=ndpost,
   
   nkeeptreedraws=ndpost,
   printevery=100L, transposed=FALSE 
)
lbart(
   x.train, y.train, x.test=matrix(0.0,0,0),
   sparse=FALSE, a=0.5, b=1, augment=FALSE, rho=NULL,
   xinfo=matrix(0.0,0,0), usequants=FALSE,
   cont=FALSE, rm.const=TRUE, tau.interval=0.95,
   k=2.0, power=2.0, base=.95, 
   binaryOffset=NULL,
   ntree=200L, numcut=100L,
   ndpost=1000L, nskip=100L,
   keepevery=1L,
   nkeeptrain=ndpost, nkeeptest=ndpost,
   
   nkeeptreedraws=ndpost,
   printevery=100L, transposed=FALSE 
)

Arguments

`x.train`	Explanatory variables for training (in sample) data. May be a matrix or a data frame, with (as usual) rows corresponding to observations and columns to variables. If a variable is a factor in a data frame, it is replaced with dummies. Note that q dummies are created if q>2 and one dummy is created if q=2, where q is the number of levels of the factor. `lbart` will generate draws of $f(x)$ for each $x$ which is a row of x.train.
`y.train`	Binary dependent variable for training (in sample) data.
`x.test`	Explanatory variables for test (out of sample) data. Should have same structure as x.train. `lbart` will generate draws of $f(x)$ for each $x$ which is a row of x.test.
`sparse`	Whether to perform variable selection based on a sparse Dirichlet prior rather than simply uniform; see Linero 2016.
`a`	Sparse parameter for $Beta(a, b)$ prior: $0.5<=a<=1$ where lower values inducing more sparsity.
`b`	Sparse parameter for $Beta(a, b)$ prior; typically, $b=1$ .
`rho`	Sparse parameter: typically $rho=p$ where $p$ is the number of covariates under consideration.
`augment`	Whether data augmentation is to be performed in sparse variable selection.
`xinfo`	You can provide the cutpoints to BART or let BART choose them for you. To provide them, use the `xinfo` argument to specify a list (matrix) where the items (rows) are the covariates and the contents of the items (columns) are the cutpoints.
`usequants`	If `usequants=FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, uniform quantiles are used for the cutpoints.
`cont`	Whether or not to assume all variables are continuous.
`rm.const`	Whether or not to remove constant variables.
`tau.interval`	The width of the interval to scale the variance for the terminal leaf values.
`k`	For numeric y, k is the number of prior standard deviations $E(Y\|x) = f(x)$ is away from +/-.5. For binary y, k is the number of prior standard deviations $f(x)$ is away from +/-3. In both cases, the bigger k is, the more conservative the fitting will be.
`power`	Power parameter for tree prior.
`base`	Base parameter for tree prior.
`binaryOffset`	Used for binary $y$ . The model is $P(Y=1 \| x) = F(f(x) + binaryOffset)$ .
`ntree`	The number of trees in the sum.
`numcut`	The number of possible values of c (see usequants). If a single number if given, this is used for all variables. Otherwise a vector with length equal to ncol(x.train) is required, where the $i^{th}$ element gives the number of c used for the $i^{th}$ variable in x.train. If usequants is false, numcut equally spaced cutoffs are used covering the range of values in the corresponding column of x.train. If usequants is true, then min(numcut, the number of unique values in the corresponding columns of x.train - 1) c values are used.
`ndpost`	The number of posterior draws returned.
`nskip`	Number of MCMC iterations to be treated as burn in.
`nkeeptrain`	Number of MCMC iterations to be returned for train data.
`nkeeptest`	Number of MCMC iterations to be returned for test data.
`nkeeptreedraws`	Number of MCMC iterations to be returned for tree draws.
`keepevery`	Every keepevery draw is kept to be returned to the user.
`printevery`	As the MCMC runs, a message is printed every printevery draws.
`transposed`	When running `lbart` in parallel, it is more memory-efficient to transpose `x.train` and `x.test`, if any, prior to calling `mc.lbart`.

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $f | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

Thus, unlike a lot of other modelling methods in R, we do not produce a single model object from which fits and summaries may be extracted. The output consists of values $f^*(x)$ where * denotes a particular draw. The $x$ is either a row from the training data (x.train) or the test data (x.test).

Value

lbart returns an object of type lbart which is essentially a list.

`yhat.train`	A matrix with ndpost rows and nrow(x.train) columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to a row of x.train. The $(i,j)$ value is $f^(x)$ for the $i^{th}$ kept draw of $f$ and the $j^{th}$ row of x.train. Burn-in is dropped.
`yhat.test`	Same as yhat.train but now the x's are the rows of the test data.
`yhat.train.mean`	train data fits = mean of yhat.train columns.
`yhat.test.mean`	test data fits = mean of yhat.test columns.
`varcount`	a matrix with ndpost rows and nrow(x.train) columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times that variable is used in a tree decision rule (over all trees) is given.

In addition, the list has a binaryOffset giving the value used.

Note that in the binary $y$ , case yhat.train and yhat.test are $f(x) + binaryOffset$ . If you want draws of the probability $P(Y=1 | x)$ you need to apply the Logistic CDF (plogis) to these values.

Examples


data(ACTG175)

## exclude those who do not have CD4 count at 96 weeks
ex <- is.na(ACTG175$cd496)
table(ex)

## inclusion criteria are CD4 counts between 200 and 500
ACTG175$cd40 <- min(500, max(250, ACTG175$cd40))

## calculate relative CD4 decline
y <- ((ACTG175$cd496-ACTG175$cd40)/ACTG175$cd40)[!ex]
summary(y)

## 0=failure, 1=success
y <- 1*(y > -0.5)

## summarize CD4 outcomes
table(y, ACTG175$arms[!ex])

table(y, ACTG175$arms[!ex])/
    matrix(table(ACTG175$arms[!ex]), nrow=2, ncol=4, byrow=TRUE)

## drop unneeded and unwanted variables
## 1: 'pidnum' patient ID number
##14: 'str2' which will be handled by strat1 below
##15: 'strat' which will be handled by strat1-strat3 below
##17: 'treat' handled by arm0-arm3 below
##18: 'offtrt' indicator of off-treatment before 96 weeks
##20: 'cd420' CD4 T cell count at 20 weeks
##21: 'cd496' CD4 T cell count at 96 weeks
##22: 'r' missing CD4 T cell count at 96 weeks
##24: 'cd820' CD8 T cell count at 20 weeks
##25: 'cens' indicator of observing the event in days
##26: 'days' number of days until the primary endpoint
##27: 'arms' handled by arm0-arm3 below
train <- as.matrix(ACTG175)[!ex, -c(1, 14:15, 17, 18, 20:22, 24:27)]
train <- cbind(1*(ACTG175$strat[!ex]==1), 1*(ACTG175$strat[!ex]==2),
               1*(ACTG175$strat[!ex]==3), train)
dimnames(train)[[2]][1:3] <- paste0('strat', 1:3)
train <- cbind(1*(ACTG175$arms[!ex]==0), 1*(ACTG175$arms[!ex]==1),
               1*(ACTG175$arms[!ex]==2), 1*(ACTG175$arms[!ex]==3), train)
dimnames(train)[[2]][1:4] <- paste0('arm', 0:3)

N <- nrow(train)

test0 <- train; test0[ , 1:4] <- 0; test0[ , 1] <- 1
test1 <- train; test1[ , 1:4] <- 0; test1[ , 2] <- 1
test2 <- train; test2[ , 1:4] <- 0; test2[ , 3] <- 1
test3 <- train; test3[ , 1:4] <- 0; test3[ , 4] <- 1

test <- rbind(test0, test1, test2, test3)

##test BART with token run to ensure installation works
## set.seed(21)
## post <- lbart(train, y, test, nskip=5, ndpost=5)

## Not run: 
set.seed(21)
post <- lbart(train, y, test)

## turn z-scores into probabilities
post$prob.test <- plogis(post$yhat.test)

## average over the posterior samples
post$prob.test.mean <- apply(post$prob.test, 2, mean)

## place estimates for arms 0-3 next to each other for convenience
itr <- cbind(post$prob.test.mean[(1:N)], post$prob.test.mean[N+(1:N)],
             post$prob.test.mean[2*N+(1:N)], post$prob.test.mean[3*N+(1:N)])

## find the BART ITR for each patient
itr.pick <- integer(N)
for(i in 1:N) itr.pick[i] <- which(itr[i, ]==max(itr[i, ]))-1

## arms 0 and 3 (monotherapy) are never chosen
table(itr.pick)

## do arms 1 and 2 show treatment heterogeneity?
diff. <- apply(post$prob.test[ , 2*N+(1:N)]-post$prob.test[ , N+(1:N)], 2, mean)
plot(sort(diff.), type='h', main='ACTG175 trial: 50% CD4 decline from baseline at 96 weeks',
     xlab='Arm 2 (1) Preferable to the Right (Left)', ylab='Prob.Diff.: Arms 2 - 1')

library(rpart)
library(rpart.plot)

## make data frame for nicer names in the plot
var <- as.data.frame(train[ , -(1:4)])

dss <- rpart(diff. ~ var$age+var$gender+var$race+var$wtkg+var$cd40+var$cd80+
                   var$karnof+var$symptom+var$hemo+var$homo+var$drugs+var$z30+
                   var$zprior+var$oprior+var$strat1+var$strat2+var$strat3,
               method='anova', control=rpart.control(cp=0.1))
rpart.plot(dss, type=3, extra=101)

## if strat1==1 (antiretroviral naive), then arm 2 is better
## otherwise, arm 1
print(dss)

all0 <- apply(post$prob.test[ , (1:N)], 1, mean)
all1 <- apply(post$prob.test[ , N+(1:N)], 1, mean)
all2 <- apply(post$prob.test[ , 2*N+(1:N)], 1, mean)
all3 <- apply(post$prob.test[ , 3*N+(1:N)], 1, mean)

## BART ITR
BART.itr <- apply(post$prob.test[ , c(N+which(itr.pick==1), 2*N+which(itr.pick==2))], 1, mean)

test <- train
test[ , 1:4] <- 0
test[test[ , 5]==0, 2] <- 1
test[test[ , 5]==1, 3] <- 1

## BART ITR simple
BART.itr.simp <- pwbart(test, post$treedraws)
BART.itr.simp <- apply(plogis(BART.itr.simp), 1, mean)

plot(density(BART.itr), xlab='Value', xlim=c(0.475, 0.775), lwd=2,
     main='ACTG175 trial: 50% CD4 decline from baseline at 96 weeks')
lines(density(BART.itr.simp), col='brown', lwd=2)
lines(density(all0), col='green', lwd=2)
lines(density(all1), col='red', lwd=2)
lines(density(all2), col='blue', lwd=2)
lines(density(all3), col='yellow', lwd=2)
legend('topleft', legend=c('All Arm 0 (ZDV only)', 'All Arm 1 (ZDV+DDI)',
                           'All Arm 2 (ZDV+DDC)', 'All Arm 3 (DDI only)',
                           'BART ITR simple', 'BART ITR'),
       col=c('green', 'red', 'blue', 'yellow', 'brown', 'black'), lty=1, lwd=2)


## End(Not run)
data(ACTG175)

## exclude those who do not have CD4 count at 96 weeks
ex <- is.na(ACTG175$cd496)
table(ex)

## inclusion criteria are CD4 counts between 200 and 500
ACTG175$cd40 <- min(500, max(250, ACTG175$cd40))

## calculate relative CD4 decline
y <- ((ACTG175$cd496-ACTG175$cd40)/ACTG175$cd40)[!ex]
summary(y)

## 0=failure, 1=success
y <- 1*(y > -0.5)

## summarize CD4 outcomes
table(y, ACTG175$arms[!ex])

table(y, ACTG175$arms[!ex])/
    matrix(table(ACTG175$arms[!ex]), nrow=2, ncol=4, byrow=TRUE)

## drop unneeded and unwanted variables
## 1: 'pidnum' patient ID number
##14: 'str2' which will be handled by strat1 below
##15: 'strat' which will be handled by strat1-strat3 below
##17: 'treat' handled by arm0-arm3 below
##18: 'offtrt' indicator of off-treatment before 96 weeks
##20: 'cd420' CD4 T cell count at 20 weeks
##21: 'cd496' CD4 T cell count at 96 weeks
##22: 'r' missing CD4 T cell count at 96 weeks
##24: 'cd820' CD8 T cell count at 20 weeks
##25: 'cens' indicator of observing the event in days
##26: 'days' number of days until the primary endpoint
##27: 'arms' handled by arm0-arm3 below
train <- as.matrix(ACTG175)[!ex, -c(1, 14:15, 17, 18, 20:22, 24:27)]
train <- cbind(1*(ACTG175$strat[!ex]==1), 1*(ACTG175$strat[!ex]==2),
               1*(ACTG175$strat[!ex]==3), train)
dimnames(train)[[2]][1:3] <- paste0('strat', 1:3)
train <- cbind(1*(ACTG175$arms[!ex]==0), 1*(ACTG175$arms[!ex]==1),
               1*(ACTG175$arms[!ex]==2), 1*(ACTG175$arms[!ex]==3), train)
dimnames(train)[[2]][1:4] <- paste0('arm', 0:3)

N <- nrow(train)

test0 <- train; test0[ , 1:4] <- 0; test0[ , 1] <- 1
test1 <- train; test1[ , 1:4] <- 0; test1[ , 2] <- 1
test2 <- train; test2[ , 1:4] <- 0; test2[ , 3] <- 1
test3 <- train; test3[ , 1:4] <- 0; test3[ , 4] <- 1

test <- rbind(test0, test1, test2, test3)

##test BART with token run to ensure installation works
## set.seed(21)
## post <- lbart(train, y, test, nskip=5, ndpost=5)

## Not run: 
set.seed(21)
post <- lbart(train, y, test)

## turn z-scores into probabilities
post$prob.test <- plogis(post$yhat.test)

## average over the posterior samples
post$prob.test.mean <- apply(post$prob.test, 2, mean)

## place estimates for arms 0-3 next to each other for convenience
itr <- cbind(post$prob.test.mean[(1:N)], post$prob.test.mean[N+(1:N)],
             post$prob.test.mean[2*N+(1:N)], post$prob.test.mean[3*N+(1:N)])

## find the BART ITR for each patient
itr.pick <- integer(N)
for(i in 1:N) itr.pick[i] <- which(itr[i, ]==max(itr[i, ]))-1

## arms 0 and 3 (monotherapy) are never chosen
table(itr.pick)

## do arms 1 and 2 show treatment heterogeneity?
diff. <- apply(post$prob.test[ , 2*N+(1:N)]-post$prob.test[ , N+(1:N)], 2, mean)
plot(sort(diff.), type='h', main='ACTG175 trial: 50% CD4 decline from baseline at 96 weeks',
     xlab='Arm 2 (1) Preferable to the Right (Left)', ylab='Prob.Diff.: Arms 2 - 1')

library(rpart)
library(rpart.plot)

## make data frame for nicer names in the plot
var <- as.data.frame(train[ , -(1:4)])

dss <- rpart(diff. ~ var$age+var$gender+var$race+var$wtkg+var$cd40+var$cd80+
                   var$karnof+var$symptom+var$hemo+var$homo+var$drugs+var$z30+
                   var$zprior+var$oprior+var$strat1+var$strat2+var$strat3,
               method='anova', control=rpart.control(cp=0.1))
rpart.plot(dss, type=3, extra=101)

## if strat1==1 (antiretroviral naive), then arm 2 is better
## otherwise, arm 1
print(dss)

all0 <- apply(post$prob.test[ , (1:N)], 1, mean)
all1 <- apply(post$prob.test[ , N+(1:N)], 1, mean)
all2 <- apply(post$prob.test[ , 2*N+(1:N)], 1, mean)
all3 <- apply(post$prob.test[ , 3*N+(1:N)], 1, mean)

## BART ITR
BART.itr <- apply(post$prob.test[ , c(N+which(itr.pick==1), 2*N+which(itr.pick==2))], 1, mean)

test <- train
test[ , 1:4] <- 0
test[test[ , 5]==0, 2] <- 1
test[test[ , 5]==1, 3] <- 1

## BART ITR simple
BART.itr.simp <- pwbart(test, post$treedraws)
BART.itr.simp <- apply(plogis(BART.itr.simp), 1, mean)

plot(density(BART.itr), xlab='Value', xlim=c(0.475, 0.775), lwd=2,
     main='ACTG175 trial: 50% CD4 decline from baseline at 96 weeks')
lines(density(BART.itr.simp), col='brown', lwd=2)
lines(density(all0), col='green', lwd=2)
lines(density(all1), col='red', lwd=2)
lines(density(all2), col='blue', lwd=2)
lines(density(all3), col='yellow', lwd=2)
legend('topleft', legend=c('All Arm 0 (ZDV only)', 'All Arm 1 (ZDV+DDI)',
                           'All Arm 2 (ZDV+DDC)', 'All Arm 3 (DDI only)',
                           'BART ITR simple', 'BART ITR'),
       col=c('green', 'red', 'blue', 'yellow', 'brown', 'black'), lty=1, lwd=2)


## End(Not run)

Bone marrow transplantation for leukemia and multi-state models

Description

137 patients with acute myelocytic leukemia (AML) and acute lymphoblastic leukemia (ALL) were given oral busulfan (Bu) 4 mg/kg on each of 4 days and intravenous cyclophosphamide (Cy) 60 mg/kg on each of 2 days (BuCy2) followed by allogeneic bone marrow transplantation from an HLA-identical or one antigen disparate sibling.

Usage

data(leukemia)data(leukemia)

Format

A data frame with 137 subjects on the following 22 variables.

G: Disease Group (1=ALL, 2=AML Low Risk in first remission, 3=AML High Risk not in first remission)
TD: Time To Death Or On Study Time
TB: Disease Free Survival Time (Time To Relapse, Death Or End Of Study)
D: Death Indicator (0=Alive, 1=Dead)
R: Relapse Indicator (0=Disease Free, 1=Relapsed)
B: Disease Free Survival Indicator (0=Alive and Disease Free, 1=Dead or Relapsed)
TA: Time To Acute Graft-Versus-Host Disease (GVHD)
A: Acute GVHD Indicator (0=Never Developed Acute GVHD, 1=Developed Acute GVHD)
TC: Time To Chronic Graft-Versus-Host Disease (GVHD)
C: Chronic GVHD Indicator (0=Never Developed Chronic GVHD, 1=Developed Chronic GVHD)
TP: Time of Platelets Returning to Normal Levels
P: Platelet Recovery Indicator (0=Platelets Never Returned to Normal, 1=Platelets Returned To Normal)
X1: Patient Age In Years
X2: Donor Age In Years
X3: Patient Gender (0=female, 1=male)
X4: Donor Gender (0=female, 1=male)
X5: Patient Cytomegalovirus (CMV) Immune Status (0=CMV Negative, 1=CMV Positive)
X6: Donor Cytomegalovirus (CMV) Immune Status (0=CMV Negative, 1=CMV Positive)
X7: Waiting Time to Transplant In Days
X8: AML Patients with Elevated Risk By French-American-British (FAB) Classification (0=Not AML/Elevated, 1=FAB M4 Or M5 with AML)
X9: Hospital (1=The Ohio State University in Columbus, 2=Alfred in Melbourne, 3=St. Vincent in Sydney, 4=Hahnemann University in Philadelphia)
X10: Methotrexate Used as a Graft-Versus-Host Disease Prophylactic (0=No, 1=Yes)

Source

Klein J. and Moeschberger M.L. (2003) Survival Analysis: Techniques for Censored and Truncated Data, New York: Springer-Verlag, 2nd Ed., Section 1.3.

References

Copelan E., Biggs J., Thompson J., Crilley P., Szer J., Klein, J., Kapoor N., Avalos, B., Cunningham I. and Atkinson, K. (1991) "Treatment for acute myelocytic leukemia with allogeneic bone marrow transplantation following preparation with BuCy2". Blood, 78(3), pp.838-843.

NCCTG Lung Cancer Data

Description

Survival in patients with advanced lung cancer from the North Central Cancer Treatment Group. Performance scores rate how well the patient can perform usual daily activities.

Format

inst:	Institution code
time:	Survival time in days
status:	censoring status 1=censored, 2=dead
age:	Age in years
sex:	Male=1 Female=2
ph.ecog:	ECOG performance score (0=good 5=dead)
ph.karno:	Karnofsky performance score (bad=0-good=100) rated by physician
pat.karno:	Karnofsky performance score as rated by patient
meal.cal:	Calories consumed at meals
wt.loss:	Weight loss in last six months

Source

Terry Therneau

References

Loprinzi CL. Laurie JA. Wieand HS. Krook JE. Novotny PJ. Kugler JW. Bartel J. Law M. Bateman M. Klatt NE. et al. Prospective evaluation of prognostic variables from patient-completed questionnaires. North Central Cancer Treatment Group. Journal of Clinical Oncology. 12(3):601-7, 1994.

Examples

data(lung)
data(lung)

Multinomial BART for categorical outcomes with fewer categories

Description

BART is a Bayesian “sum-of-trees” model.
For numeric response $y$ , we have $y = f(x) +\epsilon$ , where $\epsilon \sim N(0, 1)$ .
For a multinomial response $y$ , $P(Y=y | x) = F(f(x))$ , where $F$ denotes the standard Normal CDF (probit link) or the standard Logistic CDF (logit link).

In both cases, $f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

mbart(
      x.train, y.train,
      x.test=matrix(0,0,0), type='pbart',
      ntype=as.integer(
          factor(type,
                 levels=c('wbart', 'pbart', 'lbart'))),
      sparse=FALSE, theta=0, omega=1,
      a=0.5, b=1, augment=FALSE, rho=NULL,
      xinfo=matrix(0,0,0), usequants=FALSE,
      rm.const=TRUE,
      k=2, power=2, base=0.95,
      tau.num=c(NA, 3, 6)[ntype],
      offset=NULL, 
      ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
      ndpost=1000L, nskip=100L,
      keepevery=c(1L, 10L, 10L)[ntype],
      printevery=100L, transposed=FALSE,
      hostname=FALSE,
      mc.cores = 2L, ## mc.bart only
      nice = 19L,    ## mc.bart only
      seed = 99L     ## mc.bart only
     )

mc.mbart(
         x.train, y.train,
         x.test=matrix(0,0,0), type='pbart',
         ntype=as.integer(
             factor(type,
                    levels=c('wbart', 'pbart', 'lbart'))),
         sparse=FALSE, theta=0, omega=1,
         a=0.5, b=1, augment=FALSE, rho=NULL,
         xinfo=matrix(0,0,0), usequants=FALSE,
         rm.const=TRUE,
         k=2, power=2, base=0.95,
         tau.num=c(NA, 3, 6)[ntype],
         offset=NULL, 
         ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
         ndpost=1000L, nskip=100L,
         keepevery=c(1L, 10L, 10L)[ntype],
         printevery=100L, transposed=FALSE,
         hostname=FALSE,
         mc.cores = 2L, ## mc.bart only
         nice = 19L,    ## mc.bart only
         seed = 99L     ## mc.bart only
        )

mbart(
      x.train, y.train,
      x.test=matrix(0,0,0), type='pbart',
      ntype=as.integer(
          factor(type,
                 levels=c('wbart', 'pbart', 'lbart'))),
      sparse=FALSE, theta=0, omega=1,
      a=0.5, b=1, augment=FALSE, rho=NULL,
      xinfo=matrix(0,0,0), usequants=FALSE,
      rm.const=TRUE,
      k=2, power=2, base=0.95,
      tau.num=c(NA, 3, 6)[ntype],
      offset=NULL, 
      ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
      ndpost=1000L, nskip=100L,
      keepevery=c(1L, 10L, 10L)[ntype],
      printevery=100L, transposed=FALSE,
      hostname=FALSE,
      mc.cores = 2L, ## mc.bart only
      nice = 19L,    ## mc.bart only
      seed = 99L     ## mc.bart only
     )

mc.mbart(
         x.train, y.train,
         x.test=matrix(0,0,0), type='pbart',
         ntype=as.integer(
             factor(type,
                    levels=c('wbart', 'pbart', 'lbart'))),
         sparse=FALSE, theta=0, omega=1,
         a=0.5, b=1, augment=FALSE, rho=NULL,
         xinfo=matrix(0,0,0), usequants=FALSE,
         rm.const=TRUE,
         k=2, power=2, base=0.95,
         tau.num=c(NA, 3, 6)[ntype],
         offset=NULL, 
         ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
         ndpost=1000L, nskip=100L,
         keepevery=c(1L, 10L, 10L)[ntype],
         printevery=100L, transposed=FALSE,
         hostname=FALSE,
         mc.cores = 2L, ## mc.bart only
         nice = 19L,    ## mc.bart only
         seed = 99L     ## mc.bart only
        )

Arguments

`x.train`	Explanatory variables for training (in sample) data. May be a matrix or a data frame, with (as usual) rows corresponding to observations and columns to variables. If a variable is a factor in a data frame, it is replaced with dummies. Note that q dummies are created if q>2 and one dummy is created if q=2, where q is the number of levels of the factor. `mbart` will generate draws of $f(x)$ for each $x$ which is a row of `x.train`.
`y.train`	Categorical dependent variable for training (in sample) data.
`x.test`	Explanatory variables for test (out of sample) data. Should have same structure as `x.train`. `mbart` will generate draws of $f(x)$ for each $x$ which is a row of `x.test`.
`type`	You can use this argument to specify the type of fit. `'pbart'` for probit BART or `'lbart'` for logit BART.
`ntype`	The integer equivalent of `type` where `'pbart'` is 2 and `'lbart'` is 3.
`sparse`	Whether to perform variable selection based on a sparse Dirichlet prior rather than simply uniform; see Linero 2016.
`theta`	Set $theta$ parameter; zero means random.
`omega`	Set $omega$ parameter; zero means random.
`a`	Sparse parameter for $Beta(a, b)$ prior: $0.5<=a<=1$ where lower values inducing more sparsity.
`b`	Sparse parameter for $Beta(a, b)$ prior; typically, $b=1$ .
`rho`	Sparse parameter: typically $rho=p$ where $p$ is the number of covariates under consideration.
`augment`	Whether data augmentation is to be performed in sparse variable selection.
`xinfo`	You can provide the cutpoints to BART or let BART choose them for you. To provide them, use the `xinfo` argument to specify a list (matrix) where the items (rows) are the covariates and the contents of the items (columns) are the cutpoints.
`usequants`	If `usequants=FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, uniform quantiles are used for the cutpoints.
`rm.const`	Whether or not to remove constant variables.
`k`	For categorical `y.train`, k is the number of prior standard deviations $f(x)$ is away from +/-3.
`power`	Power parameter for tree prior.
`base`	Base parameter for tree prior.
`tau.num`	The numerator in the `tau` definition, i.e., `tau=tau.num/(k*sqrt(ntree))`.
`offset`	With Multinomial BART, the centering is $P(yj=1 \| x) = F(fj(x) + offset[j])$ where `offset` defaults to `F^{-1}(mean(y.train))`. You can use the `offset` parameter to over-ride these defaults.
`ntree`	The number of trees in the sum.
`numcut`	The number of possible values of c (see usequants). If a single number if given, this is used for all variables. Otherwise a vector with length equal to ncol(x.train) is required, where the $i^{th}$ element gives the number of c used for the $i^{th}$ variable in x.train. If usequants is false, numcut equally spaced cutoffs are used covering the range of values in the corresponding column of x.train. If usequants is true, then min(numcut, the number of unique values in the corresponding columns of x.train - 1) c values are used.
`ndpost`	The number of posterior draws returned.
`nskip`	Number of MCMC iterations to be treated as burn in.
`keepevery`	Every keepevery draw is kept to be returned to the user.
`printevery`	As the MCMC runs, a message is printed every printevery draws.
`transposed`	When running `mbart` in parallel, it is more memory-efficient to transpose `x.train` and `x.test`, if any, prior to calling `mc.mbart`.
`hostname`	When running on a cluster occasionally it is useful to track on which node each chain is running; to do so set this argument to `TRUE`.
`seed`	Setting the seed required for reproducible MCMC.
`mc.cores`	Number of cores to employ in parallel.
`nice`	Set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest).

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from $f$ in the categorical $y$ case.

Value

mbart returns an object of type mbart which is essentially a list.

`yhat.train`	A matrix with `ndpost` rows and `nrow(x.train)K` columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to an estimate for a row of `x.train`. For the `i`th row of `x.train`, we provide the corresponding `(i-1)*K+j`th column of `yhat.train` where `j=1,...,K` indexes the categories. Burn-in is dropped.
`yhat.train.mean`	train data fits = mean of `yhat.train` columns.
`varcount`	a matrix with `ndpost` rows and `nrow(x.train)` columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times that variable is used in a tree decision rule (over all trees) is given.

In addition, the list has a offset vector giving the value used.

Note that in the multinomial $y$ case yhat.train is $f(x) + offset[j]$ .

Examples


N=500
set.seed(12)
x1=runif(N)
x2=runif(N, max=1-x1)
x3=1-x1-x2
x.train=cbind(x1, x2, x3)
y.train=0
for(i in 1:N)
    y.train[i]=sum((1:3)*rmultinom(1, 1, x.train[i, ]))
table(y.train)/N

##test mbart with token run to ensure installation works
set.seed(99)
post = mbart(x.train, y.train, nskip=1, ndpost=1)

## Not run: 
set.seed(99)
post=mbart(x.train, y.train, x.train)
##mc.post=mbart(x.train, y.train, x.test, mc.cores=8, seed=99)

K=3
i=seq(1, N*K, K)-1
for(j in 1:K)
    print(cor(x.train[ , j], post$prob.test.mean[i+j])^2)


## End(Not run)
N=500
set.seed(12)
x1=runif(N)
x2=runif(N, max=1-x1)
x3=1-x1-x2
x.train=cbind(x1, x2, x3)
y.train=0
for(i in 1:N)
    y.train[i]=sum((1:3)*rmultinom(1, 1, x.train[i, ]))
table(y.train)/N

##test mbart with token run to ensure installation works
set.seed(99)
post = mbart(x.train, y.train, nskip=1, ndpost=1)

## Not run: 
set.seed(99)
post=mbart(x.train, y.train, x.train)
##mc.post=mbart(x.train, y.train, x.test, mc.cores=8, seed=99)

K=3
i=seq(1, N*K, K)-1
for(j in 1:K)
    print(cor(x.train[ , j], post$prob.test.mean[i+j])^2)


## End(Not run)

Multinomial BART for categorical outcomes with more categories

Description

In both cases, $f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

mbart2(
      x.train, y.train,
      x.test=matrix(0,0,0), type='lbart',
      ntype=as.integer(
          factor(type,
                 levels=c('wbart', 'pbart', 'lbart'))),
      sparse=FALSE, theta=0, omega=1,
      a=0.5, b=1, augment=FALSE, rho=NULL,
      xinfo=matrix(0,0,0), usequants=FALSE,
      rm.const=TRUE,
      k=2, power=2, base=0.95,
      tau.num=c(NA, 3, 6)[ntype],
      offset=NULL, 
      ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
      ndpost=1000L, nskip=100L,
      keepevery=c(1L, 10L, 10L)[ntype],
      printevery=100L, transposed=FALSE,
      hostname=FALSE,
      mc.cores = 2L, ## mc.bart only
      nice = 19L,    ## mc.bart only
      seed = 99L     ## mc.bart only
     )

mc.mbart2(
         x.train, y.train,
         x.test=matrix(0,0,0), type='lbart',
         ntype=as.integer(
             factor(type,
                    levels=c('wbart', 'pbart', 'lbart'))),
         sparse=FALSE, theta=0, omega=1,
         a=0.5, b=1, augment=FALSE, rho=NULL,
         xinfo=matrix(0,0,0), usequants=FALSE,
         rm.const=TRUE,
         k=2, power=2, base=0.95,
         tau.num=c(NA, 3, 6)[ntype],
         offset=NULL, 
         ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
         ndpost=1000L, nskip=100L,
         keepevery=c(1L, 10L, 10L)[ntype],
         printevery=100L, transposed=FALSE,
         hostname=FALSE,
         mc.cores = 2L, ## mc.bart only
         nice = 19L,    ## mc.bart only
         seed = 99L     ## mc.bart only
        )

mbart2(
      x.train, y.train,
      x.test=matrix(0,0,0), type='lbart',
      ntype=as.integer(
          factor(type,
                 levels=c('wbart', 'pbart', 'lbart'))),
      sparse=FALSE, theta=0, omega=1,
      a=0.5, b=1, augment=FALSE, rho=NULL,
      xinfo=matrix(0,0,0), usequants=FALSE,
      rm.const=TRUE,
      k=2, power=2, base=0.95,
      tau.num=c(NA, 3, 6)[ntype],
      offset=NULL, 
      ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
      ndpost=1000L, nskip=100L,
      keepevery=c(1L, 10L, 10L)[ntype],
      printevery=100L, transposed=FALSE,
      hostname=FALSE,
      mc.cores = 2L, ## mc.bart only
      nice = 19L,    ## mc.bart only
      seed = 99L     ## mc.bart only
     )

mc.mbart2(
         x.train, y.train,
         x.test=matrix(0,0,0), type='lbart',
         ntype=as.integer(
             factor(type,
                    levels=c('wbart', 'pbart', 'lbart'))),
         sparse=FALSE, theta=0, omega=1,
         a=0.5, b=1, augment=FALSE, rho=NULL,
         xinfo=matrix(0,0,0), usequants=FALSE,
         rm.const=TRUE,
         k=2, power=2, base=0.95,
         tau.num=c(NA, 3, 6)[ntype],
         offset=NULL, 
         ntree=c(200L, 50L, 50L)[ntype], numcut=100L,
         ndpost=1000L, nskip=100L,
         keepevery=c(1L, 10L, 10L)[ntype],
         printevery=100L, transposed=FALSE,
         hostname=FALSE,
         mc.cores = 2L, ## mc.bart only
         nice = 19L,    ## mc.bart only
         seed = 99L     ## mc.bart only
        )

Arguments

`x.train`	Explanatory variables for training (in sample) data. May be a matrix or a data frame, with (as usual) rows corresponding to observations and columns to variables. If a variable is a factor in a data frame, it is replaced with dummies. Note that q dummies are created if q>2 and one dummy is created if q=2, where q is the number of levels of the factor. `mbart2` will generate draws of $f(x)$ for each $x$ which is a row of `x.train`.
`y.train`	Categorical dependent variable for training (in sample) data.
`x.test`	Explanatory variables for test (out of sample) data. Should have same structure as `x.train`. `mbart2` will generate draws of $f(x)$ for each $x$ which is a row of `x.test`.
`type`	You can use this argument to specify the type of fit. `'pbart'` for probit BART or `'lbart'` for logit BART.
`ntype`	The integer equivalent of `type` where `'pbart'` is 2 and `'lbart'` is 3.
`sparse`	Whether to perform variable selection based on a sparse Dirichlet prior rather than simply uniform; see Linero 2016.
`theta`	Set $theta$ parameter; zero means random.
`omega`	Set $omega$ parameter; zero means random.
`a`	Sparse parameter for $Beta(a, b)$ prior: $0.5<=a<=1$ where lower values inducing more sparsity.
`b`	Sparse parameter for $Beta(a, b)$ prior; typically, $b=1$ .
`rho`	Sparse parameter: typically $rho=p$ where $p$ is the number of covariates under consideration.
`augment`	Whether data augmentation is to be performed in sparse variable selection.
`xinfo`	You can provide the cutpoints to BART or let BART choose them for you. To provide them, use the `xinfo` argument to specify a list (matrix) where the items (rows) are the covariates and the contents of the items (columns) are the cutpoints.
`usequants`	If `usequants=FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, uniform quantiles are used for the cutpoints.
`rm.const`	Whether or not to remove constant variables.
`k`	For categorical `y.train`, k is the number of prior standard deviations $f(x)$ is away from +/-3.
`power`	Power parameter for tree prior.
`base`	Base parameter for tree prior.
`tau.num`	The numerator in the `tau` definition, i.e., `tau=tau.num/(k*sqrt(ntree))`.
`offset`	With Multinomial BART, the centering is $P(yj=1 \| x) = F(fj(x) + offset[j])$ where `offset` defaults to `F^{-1}(mean(y.train))`. You can use the `offset` parameter to over-ride these defaults.
`ntree`	The number of trees in the sum.
`numcut`	The number of possible values of c (see usequants). If a single number if given, this is used for all variables. Otherwise a vector with length equal to ncol(x.train) is required, where the $i^{th}$ element gives the number of c used for the $i^{th}$ variable in x.train. If usequants is false, numcut equally spaced cutoffs are used covering the range of values in the corresponding column of x.train. If usequants is true, then min(numcut, the number of unique values in the corresponding columns of x.train - 1) c values are used.
`ndpost`	The number of posterior draws returned.
`nskip`	Number of MCMC iterations to be treated as burn in.
`keepevery`	Every keepevery draw is kept to be returned to the user.
`printevery`	As the MCMC runs, a message is printed every printevery draws.
`transposed`	When running `mbart2` in parallel, it is more memory-efficient to transpose `x.train` and `x.test`, if any, prior to calling `mc.mbart2`.
`hostname`	When running on a cluster occasionally it is useful to track on which node each chain is running; to do so set this argument to `TRUE`.
`seed`	Setting the seed required for reproducible MCMC.
`mc.cores`	Number of cores to employ in parallel.
`nice`	Set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest).

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from $f$ in the categorical $y$ case.

Value

mbart2 returns an object of type mbart2 which is essentially a list.

`yhat.train`	A matrix with `ndpost` rows and `nrow(x.train)K` columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to an estimate for a row of `x.train`. For the `i`th row of `x.train`, we provide the corresponding `(i-1)*K+j`th column of `yhat.train` where `j=1,...,K` indexes the categories. Burn-in is dropped.
`yhat.train.mean`	train data fits = mean of `yhat.train` columns.
`varcount`	a matrix with `ndpost` rows and `nrow(x.train)` columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times that variable is used in a tree decision rule (over all trees) is given.

In addition, the list has a offset vector giving the value used.

Note that in the multinomial $y$ case yhat.train is $f(x) + offset[j]$ .

Examples


N=500
set.seed(12)
x1=runif(N)
x2=runif(N, max=1-x1)
x3=1-x1-x2
x.train=cbind(x1, x2, x3)
y.train=0
for(i in 1:N)
    y.train[i]=sum((1:3)*rmultinom(1, 1, x.train[i, ]))
table(y.train)/N

##test mbart2 with token run to ensure installation works
set.seed(99)
post = mbart2(x.train, y.train, nskip=1, ndpost=1)

## Not run: 
set.seed(99)
post=mbart2(x.train, y.train, x.train)
##mc.post=mbart2(x.train, y.train, x.test, mc.cores=8, seed=99)

K=3
i=seq(1, N*K, K)-1
for(j in 1:K)
    print(cor(x.train[ , j], post$prob.test.mean[i+j])^2)


## End(Not run)
N=500
set.seed(12)
x1=runif(N)
x2=runif(N, max=1-x1)
x3=1-x1-x2
x.train=cbind(x1, x2, x3)
y.train=0
for(i in 1:N)
    y.train[i]=sum((1:3)*rmultinom(1, 1, x.train[i, ]))
table(y.train)/N

##test mbart2 with token run to ensure installation works
set.seed(99)
post = mbart2(x.train, y.train, nskip=1, ndpost=1)

## Not run: 
set.seed(99)
post=mbart2(x.train, y.train, x.train)
##mc.post=mbart2(x.train, y.train, x.test, mc.cores=8, seed=99)

K=3
i=seq(1, N*K, K)-1
for(j in 1:K)
    print(cor(x.train[ , j], post$prob.test.mean[i+j])^2)


## End(Not run)

Detecting OpenMP

Description

This package was designed for OpenMP. For example, the pwbart function can use OpenMP or the parallel R package for multi-threading. On UNIX/Unix-like systems, OpenMP, if available, is discovered at install time; for the details, see the configure.ac file which can be found in the source version of this package. However, we know of no GPL licensed code available to detect OpenMP on Windows (for Artistic licensed OpenMP detection code on Windows, see the Bioconductor R package rGADEM). To determine whether OpenMP is available at run time, we provide the function documented here.

Usage

mc.cores.openmp()
mc.cores.openmp()

Value

Returns a zero when OpenMP is not available, otherwise, an integer greater than zero when OpenMP is available (returns one unless you are running in a multi-threaded process).

Examples


mc.cores.openmp()

mc.cores.openmp()

Predicting new observations with a previously fitted BART model

Description

BART is a Bayesian “sum-of-trees” model.
For a numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .

$f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

mc.crisk.pwbart( x.test, x.test2,
                 treedraws, treedraws2,
                 binaryOffset=0, binaryOffset2=0,
                 mc.cores=2L, type='pbart',
                 transposed=FALSE, nice=19L
               )
mc.crisk.pwbart( x.test, x.test2,
                 treedraws, treedraws2,
                 binaryOffset=0, binaryOffset2=0,
                 mc.cores=2L, type='pbart',
                 transposed=FALSE, nice=19L
               )

Arguments

`x.test`	Matrix of covariates to predict $y$ for cause 1.
`x.test2`	Matrix of covariates to predict $y$ for cause 2.
`treedraws`	`$treedraws` for cause 1.
`treedraws2`	`$treedraws` for cause 2.
`binaryOffset`	Mean to add on to $y$ prediction for cause 1.
`binaryOffset2`	Mean to add on to $y$ prediction for cause 2.
`mc.cores`	Number of threads to utilize.
`type`	Whether to employ Albert-Chib, `'pbart'`, or Holmes-Held, `'lbart'`.
`transposed`	When running `pwbart` or `mc.pwbart` in parallel, it is more memory-efficient to transpose `x.test` prior to calling the internal versions of these functions.
`nice`	Set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest).

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

Value

Returns an object of type criskbart which is essentially a list with components:

`yhat.test`	A matrix with ndpost rows and nrow(x.test) columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to a row of x.train. The $(i,j)$ value is $f^(x)$ for the $i^{th}$ kept draw of $f$ and the $j^{th}$ row of x.train. Burn-in is dropped.
`surv.test`	test data fits for survival probability.
`surv.test.mean`	mean of `surv.test` over the posterior samples.
`prob.test`	The probability of suffering cause 1 which is occasionally useful, e.g., in calculating the concordance.
`prob.test2`	The probability of suffering cause 2 which is occasionally useful, e.g., in calculating the concordance.
`cif.test`	The cumulative incidence function of cause 1, $F_1(t, x)$ , where x's are the rows of the test data.
`cif.test2`	The cumulative incidence function of cause 2, $F_2(t, x)$ , where x's are the rows of the test data.
`yhat.test.mean`	test data fits = mean of yhat.test columns.
`cif.test.mean`	mean of `cif.test` columns for cause 1.
`cif.test2.mean`	mean of `cif.test2` columns for cause 2.

Examples


data(transplant)

delta <- (as.numeric(transplant$event)-1)
## recode so that delta=1 is cause of interest; delta=2 otherwise
delta[delta==1] <- 4
delta[delta==2] <- 1
delta[delta>1] <- 2
table(delta, transplant$event)

times <- pmax(1, ceiling(transplant$futime/7)) ## weeks
##times <- pmax(1, ceiling(transplant$futime/30.5)) ## months
table(times)

typeO <- 1*(transplant$abo=='O')
typeA <- 1*(transplant$abo=='A')
typeB <- 1*(transplant$abo=='B')
typeAB <- 1*(transplant$abo=='AB')
table(typeA, typeO)

x.train <- cbind(typeO, typeA, typeB, typeAB)

x.test <- cbind(1, 0, 0, 0)
dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

## parallel::mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
        post <- mc.crisk.bart(x.train=x.train, times=times, delta=delta,
                               seed=99, mc.cores=2, nskip=5, ndpost=5,
                               keepevery=1)

        pre <- surv.pre.bart(x.train=x.train, x.test=x.test,
                             times=times, delta=delta)

        K <- post$K

        pred <- mc.crisk.pwbart(pre$tx.test, pre$tx.test,
                                post$treedraws, post$treedraws2,
                                post$binaryOffset, post$binaryOffset2)
}

## Not run: 

## run one long MCMC chain in one process
## set.seed(99)
## post <- crisk.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

## in the interest of time, consider speeding it up by parallel processing
## run "mc.cores" number of shorter MCMC chains in parallel processes
post <- mc.crisk.bart(x.train=x.train,
                       times=times, delta=delta,
                       x.test=x.test, seed=99, mc.cores=8)

check <- mc.crisk.pwbart(post$tx.test, post$tx.test,
                          post$treedraws, post$treedraws2,
                          post$binaryOffset,
                          post$binaryOffset2, mc.cores=8)
## check <- predict(post, newdata=post$tx.test, newdata2=post$tx.test2,
##                  mc.cores=8)

print(c(post$surv.test.mean[1], check$surv.test.mean[1],
        post$surv.test.mean[1]-check$surv.test.mean[1]), digits=22)

print(all(round(post$surv.test.mean, digits=9)==
    round(check$surv.test.mean, digits=9)))

print(c(post$cif.test.mean[1], check$cif.test.mean[1],
        post$cif.test.mean[1]-check$cif.test.mean[1]), digits=22)

print(all(round(post$cif.test.mean, digits=9)==
    round(check$cif.test.mean, digits=9)))

print(c(post$cif.test2.mean[1], check$cif.test2.mean[1],
        post$cif.test2.mean[1]-check$cif.test2.mean[1]), digits=22)

print(all(round(post$cif.test2.mean, digits=9)==
    round(check$cif.test2.mean, digits=9)))


## End(Not run)
data(transplant)

delta <- (as.numeric(transplant$event)-1)
## recode so that delta=1 is cause of interest; delta=2 otherwise
delta[delta==1] <- 4
delta[delta==2] <- 1
delta[delta>1] <- 2
table(delta, transplant$event)

times <- pmax(1, ceiling(transplant$futime/7)) ## weeks
##times <- pmax(1, ceiling(transplant$futime/30.5)) ## months
table(times)

typeO <- 1*(transplant$abo=='O')
typeA <- 1*(transplant$abo=='A')
typeB <- 1*(transplant$abo=='B')
typeAB <- 1*(transplant$abo=='AB')
table(typeA, typeO)

x.train <- cbind(typeO, typeA, typeB, typeAB)

x.test <- cbind(1, 0, 0, 0)
dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

## parallel::mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
        post <- mc.crisk.bart(x.train=x.train, times=times, delta=delta,
                               seed=99, mc.cores=2, nskip=5, ndpost=5,
                               keepevery=1)

        pre <- surv.pre.bart(x.train=x.train, x.test=x.test,
                             times=times, delta=delta)

        K <- post$K

        pred <- mc.crisk.pwbart(pre$tx.test, pre$tx.test,
                                post$treedraws, post$treedraws2,
                                post$binaryOffset, post$binaryOffset2)
}

## Not run: 

## run one long MCMC chain in one process
## set.seed(99)
## post <- crisk.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

## in the interest of time, consider speeding it up by parallel processing
## run "mc.cores" number of shorter MCMC chains in parallel processes
post <- mc.crisk.bart(x.train=x.train,
                       times=times, delta=delta,
                       x.test=x.test, seed=99, mc.cores=8)

check <- mc.crisk.pwbart(post$tx.test, post$tx.test,
                          post$treedraws, post$treedraws2,
                          post$binaryOffset,
                          post$binaryOffset2, mc.cores=8)
## check <- predict(post, newdata=post$tx.test, newdata2=post$tx.test2,
##                  mc.cores=8)

print(c(post$surv.test.mean[1], check$surv.test.mean[1],
        post$surv.test.mean[1]-check$surv.test.mean[1]), digits=22)

print(all(round(post$surv.test.mean, digits=9)==
    round(check$surv.test.mean, digits=9)))

print(c(post$cif.test.mean[1], check$cif.test.mean[1],
        post$cif.test.mean[1]-check$cif.test.mean[1]), digits=22)

print(all(round(post$cif.test.mean, digits=9)==
    round(check$cif.test.mean, digits=9)))

print(c(post$cif.test2.mean[1], check$cif.test2.mean[1],
        post$cif.test2.mean[1]-check$cif.test2.mean[1]), digits=22)

print(all(round(post$cif.test2.mean, digits=9)==
    round(check$cif.test2.mean, digits=9)))


## End(Not run)

Predicting new observations with a previously fitted BART model

Description

BART is a Bayesian “sum-of-trees” model.
For a numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .

$f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

mc.crisk2.pwbart( x.test, x.test2,
                 treedraws, treedraws2,
                 binaryOffset=0, binaryOffset2=0,
                 mc.cores=2L, type='pbart',
                 transposed=FALSE, nice=19L
               )
mc.crisk2.pwbart( x.test, x.test2,
                 treedraws, treedraws2,
                 binaryOffset=0, binaryOffset2=0,
                 mc.cores=2L, type='pbart',
                 transposed=FALSE, nice=19L
               )

Arguments

`x.test`	Matrix of covariates to predict $y$ for cause 1.
`x.test2`	Matrix of covariates to predict $y$ for cause 2.
`treedraws`	`$treedraws` for cause 1.
`treedraws2`	`$treedraws` for cause 2.
`binaryOffset`	Mean to add on to $y$ prediction for cause 1.
`binaryOffset2`	Mean to add on to $y$ prediction for cause 2.
`mc.cores`	Number of threads to utilize.
`type`	Whether to employ Albert-Chib, `'pbart'`, or Holmes-Held, `'lbart'`.
`transposed`	When running `pwbart` or `mc.pwbart` in parallel, it is more memory-efficient to transpose `x.test` prior to calling the internal versions of these functions.
`nice`	Set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest).

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

Value

Returns an object of type crisk2bart which is essentially a list with components:

`yhat.test`	A matrix with ndpost rows and nrow(x.test) columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to a row of x.train. The $(i,j)$ value is $f^(x)$ for the $i^{th}$ kept draw of $f$ and the $j^{th}$ row of x.train. Burn-in is dropped.
`surv.test`	test data fits for survival probability.
`surv.test.mean`	mean of `surv.test` over the posterior samples.
`prob.test`	The probability of suffering cause 1 which is occasionally useful, e.g., in calculating the concordance.
`prob.test2`	The probability of suffering cause 2 which is occasionally useful, e.g., in calculating the concordance.
`cif.test`	The cumulative incidence function of cause 1, $F_1(t, x)$ , where x's are the rows of the test data.
`cif.test2`	The cumulative incidence function of cause 2, $F_2(t, x)$ , where x's are the rows of the test data.
`yhat.test.mean`	test data fits = mean of yhat.test columns.
`cif.test.mean`	mean of `cif.test` columns for cause 1.
`cif.test2.mean`	mean of `cif.test2` columns for cause 2.

Examples


data(transplant)

delta <- (as.numeric(transplant$event)-1)
## recode so that delta=1 is cause of interest; delta=2 otherwise
delta[delta==1] <- 4
delta[delta==2] <- 1
delta[delta>1] <- 2
table(delta, transplant$event)

times <- pmax(1, ceiling(transplant$futime/7)) ## weeks
##times <- pmax(1, ceiling(transplant$futime/30.5)) ## months
table(times)

typeO <- 1*(transplant$abo=='O')
typeA <- 1*(transplant$abo=='A')
typeB <- 1*(transplant$abo=='B')
typeAB <- 1*(transplant$abo=='AB')
table(typeA, typeO)

x.train <- cbind(typeO, typeA, typeB, typeAB)

x.test <- cbind(1, 0, 0, 0)
dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

## parallel::mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
        post <- mc.crisk2.bart(x.train=x.train, times=times, delta=delta,
                               seed=99, mc.cores=2, nskip=5, ndpost=5,
                               keepevery=1)

        pre <- surv.pre.bart(x.train=x.train, x.test=x.test,
                             times=times, delta=delta)

        K <- post$K

        pred <- mc.crisk2.pwbart(pre$tx.test, pre$tx.test,
                                post$treedraws, post$treedraws2,
                                post$binaryOffset, post$binaryOffset2)
}

## Not run: 

## run one long MCMC chain in one process
## set.seed(99)
## post <- crisk2.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

## in the interest of time, consider speeding it up by parallel processing
## run "mc.cores" number of shorter MCMC chains in parallel processes
post <- mc.crisk2.bart(x.train=x.train,
                       times=times, delta=delta,
                       x.test=x.test, seed=99, mc.cores=8)

check <- mc.crisk2.pwbart(post$tx.test, post$tx.test,
                          post$treedraws, post$treedraws2,
                          post$binaryOffset,
                          post$binaryOffset2, mc.cores=8)
## check <- predict(post, newdata=post$tx.test, newdata2=post$tx.test2,
##                  mc.cores=8)

print(c(post$surv.test.mean[1], check$surv.test.mean[1],
        post$surv.test.mean[1]-check$surv.test.mean[1]), digits=22)

print(all(round(post$surv.test.mean, digits=9)==
    round(check$surv.test.mean, digits=9)))

print(c(post$cif.test.mean[1], check$cif.test.mean[1],
        post$cif.test.mean[1]-check$cif.test.mean[1]), digits=22)

print(all(round(post$cif.test.mean, digits=9)==
    round(check$cif.test.mean, digits=9)))

print(c(post$cif.test2.mean[1], check$cif.test2.mean[1],
        post$cif.test2.mean[1]-check$cif.test2.mean[1]), digits=22)

print(all(round(post$cif.test2.mean, digits=9)==
    round(check$cif.test2.mean, digits=9)))



## End(Not run)
data(transplant)

delta <- (as.numeric(transplant$event)-1)
## recode so that delta=1 is cause of interest; delta=2 otherwise
delta[delta==1] <- 4
delta[delta==2] <- 1
delta[delta>1] <- 2
table(delta, transplant$event)

times <- pmax(1, ceiling(transplant$futime/7)) ## weeks
##times <- pmax(1, ceiling(transplant$futime/30.5)) ## months
table(times)

typeO <- 1*(transplant$abo=='O')
typeA <- 1*(transplant$abo=='A')
typeB <- 1*(transplant$abo=='B')
typeAB <- 1*(transplant$abo=='AB')
table(typeA, typeO)

x.train <- cbind(typeO, typeA, typeB, typeAB)

x.test <- cbind(1, 0, 0, 0)
dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

## parallel::mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
        post <- mc.crisk2.bart(x.train=x.train, times=times, delta=delta,
                               seed=99, mc.cores=2, nskip=5, ndpost=5,
                               keepevery=1)

        pre <- surv.pre.bart(x.train=x.train, x.test=x.test,
                             times=times, delta=delta)

        K <- post$K

        pred <- mc.crisk2.pwbart(pre$tx.test, pre$tx.test,
                                post$treedraws, post$treedraws2,
                                post$binaryOffset, post$binaryOffset2)
}

## Not run: 

## run one long MCMC chain in one process
## set.seed(99)
## post <- crisk2.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

## in the interest of time, consider speeding it up by parallel processing
## run "mc.cores" number of shorter MCMC chains in parallel processes
post <- mc.crisk2.bart(x.train=x.train,
                       times=times, delta=delta,
                       x.test=x.test, seed=99, mc.cores=8)

check <- mc.crisk2.pwbart(post$tx.test, post$tx.test,
                          post$treedraws, post$treedraws2,
                          post$binaryOffset,
                          post$binaryOffset2, mc.cores=8)
## check <- predict(post, newdata=post$tx.test, newdata2=post$tx.test2,
##                  mc.cores=8)

print(c(post$surv.test.mean[1], check$surv.test.mean[1],
        post$surv.test.mean[1]-check$surv.test.mean[1]), digits=22)

print(all(round(post$surv.test.mean, digits=9)==
    round(check$surv.test.mean, digits=9)))

print(c(post$cif.test.mean[1], check$cif.test.mean[1],
        post$cif.test.mean[1]-check$cif.test.mean[1]), digits=22)

print(all(round(post$cif.test.mean, digits=9)==
    round(check$cif.test.mean, digits=9)))

print(c(post$cif.test2.mean[1], check$cif.test2.mean[1],
        post$cif.test2.mean[1]-check$cif.test2.mean[1]), digits=22)

print(all(round(post$cif.test2.mean, digits=9)==
    round(check$cif.test2.mean, digits=9)))



## End(Not run)

Logit BART for dichotomous outcomes with Logistic latents and parallel computation

Description

In both cases, $f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

mc.lbart(
   x.train, y.train, x.test=matrix(0.0,0,0),
   sparse=FALSE, a=0.5, b=1, augment=FALSE, rho=NULL,
   xinfo=matrix(0.0,0,0), usequants=FALSE,
   cont=FALSE, rm.const=TRUE, tau.interval=0.95,
   k=2.0, power=2.0, base=.95,
   binaryOffset=NULL,
   ntree=50L, numcut=100L,
   ndpost=1000L, nskip=100L,
   keepevery=1L, printevery=100,
   keeptrainfits=TRUE, transposed=FALSE,
   
   mc.cores = 2L, nice = 19L,
   seed = 99L
)
mc.lbart(
   x.train, y.train, x.test=matrix(0.0,0,0),
   sparse=FALSE, a=0.5, b=1, augment=FALSE, rho=NULL,
   xinfo=matrix(0.0,0,0), usequants=FALSE,
   cont=FALSE, rm.const=TRUE, tau.interval=0.95,
   k=2.0, power=2.0, base=.95,
   binaryOffset=NULL,
   ntree=50L, numcut=100L,
   ndpost=1000L, nskip=100L,
   keepevery=1L, printevery=100,
   keeptrainfits=TRUE, transposed=FALSE,
   
   mc.cores = 2L, nice = 19L,
   seed = 99L
)

Arguments

`x.train`	Explanatory variables for training (in sample) data. May be a matrix or a data frame, with (as usual) rows corresponding to observations and columns to variables. If a variable is a factor in a data frame, it is replaced with dummies. Note that q dummies are created if q>2 and one dummy is created if q=2, where q is the number of levels of the factor. `lbart` will generate draws of $f(x)$ for each $x$ which is a row of x.train.
`y.train`	Dependent variable for training (in sample) data. If y is numeric a continous response model is fit (normal errors). If y is a factor (or just has values 0 and 1) then a binary response model with a logit link is fit.
`x.test`	Explanatory variables for test (out of sample) data. Should have same structure as x.train. `lbart` will generate draws of $f(x)$ for each $x$ which is a row of x.test.
`sparse`	Whether to perform variable selection based on a sparse Dirichlet prior rather than simply uniform; see Linero 2016.
`a`	Sparse parameter for $Beta(a, b)$ prior: $0.5<=a<=1$ where lower values inducing more sparsity.
`b`	Sparse parameter for $Beta(a, b)$ prior; typically, $b=1$ .
`rho`	Sparse parameter: typically $rho=p$ where $p$ is the number of covariates under consideration.
`augment`	Whether data augmentation is to be performed in sparse variable selection.
`xinfo`	You can provide the cutpoints to BART or let BART choose them for you. To provide them, use the `xinfo` argument to specify a list (matrix) where the items (rows) are the covariates and the contents of the items (columns) are the cutpoints.
`usequants`	If `usequants=FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, uniform quantiles are used for the cutpoints.
`cont`	Whether or not to assume all variables are continuous.
`rm.const`	Whether or not to remove constant variables.
`tau.interval`	The width of the interval to scale the variance for the terminal leaf values.
`k`	For numeric y, k is the number of prior standard deviations $E(Y\|x) = f(x)$ is away from +/-.5. For binary y, k is the number of prior standard deviations $f(x)$ is away from +/-3. In both cases, the bigger k is, the more conservative the fitting will be.
`power`	Power parameter for tree prior.
`base`	Base parameter for tree prior.
`binaryOffset`	Used for binary $y$ . The model is $P(Y=1 \| x) = F(f(x) + binaryOffset)$ .
`ntree`	The number of trees in the sum.
`numcut`	The number of possible values of c (see usequants). If a single number if given, this is used for all variables. Otherwise a vector with length equal to ncol(x.train) is required, where the $i^{th}$ element gives the number of c used for the $i^{th}$ variable in x.train. If usequants is false, numcut equally spaced cutoffs are used covering the range of values in the corresponding column of x.train. If usequants is true, then min(numcut, the number of unique values in the corresponding columns of x.train - 1) c values are used.
`ndpost`	The number of posterior draws returned.
`nskip`	Number of MCMC iterations to be treated as burn in.
`keepevery`	Every keepevery draw is kept to be returned to the user.
`printevery`	As the MCMC runs, a message is printed every printevery draws.
`keeptrainfits`	Whether to keep `yhat.train` or not.
`transposed`	When running `lbart` in parallel, it is more memory-efficient to transpose `x.train` and `x.test`, if any, prior to calling `mc.lbart`.
`seed`	Setting the seed required for reproducible MCMC.
`mc.cores`	Number of cores to employ in parallel.
`nice`	Set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest).

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

Value

mc.lbart returns an object of type lbart which is essentially a list.

`yhat.train`	A matrix with ndpost rows and nrow(x.train) columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to a row of x.train. The $(i,j)$ value is $f^(x)$ for the $i^{th}$ kept draw of $f$ and the $j^{th}$ row of x.train. Burn-in is dropped.
`yhat.test`	Same as yhat.train but now the x's are the rows of the test data.
`yhat.train.mean`	train data fits = mean of yhat.train columns.
`yhat.test.mean`	test data fits = mean of yhat.test columns.
`varcount`	a matrix with ndpost rows and nrow(x.train) columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times that variable is used in a tree decision rule (over all trees) is given.

In addition, the list has a binaryOffset giving the value used.

Note that in the binary $y$ , case yhat.train and yhat.test are $f(x) + binaryOffset$ . If you want draws of the probability $P(Y=1 | x)$ you need to apply the Logistic cdf (plogis) to these values.

Examples

set.seed(99)
n=5000
x = sort(-2+4*runif(n))
X=matrix(x,ncol=1)
f = function(x) {return((1/2)*x^3)}
FL = function(x) {return(exp(x)/(1+exp(x)))}
pv = FL(f(x))
y = rbinom(n,1,pv)
np=100
xp=-2+4*(1:np)/np
Xp=matrix(xp,ncol=1)

## parallel::mcparallel/mccollect do not exist on windows
## if(.Platform$OS.type=='unix') {
## ##test BART with token run to ensure installation works
##     mf = mc.lbart(X, y, nskip=5, ndpost=5, mc.cores=1, seed=99)
## }

## Not run: 
set.seed(99)
pf = lbart(X,y,Xp)

plot(f(Xp), pf$yhat.test.mean, xlim=c(-4, 4), ylim=c(-4, 4),
     xlab='True f(x)', ylab='BART f(x)')
lines(c(-4, 4), c(-4, 4))

mf = mc.lbart(X,y,Xp, mc.cores=4, seed=99)

plot(f(Xp), mf$yhat.test.mean, xlim=c(-4, 4), ylim=c(-4, 4),
     xlab='True f(x)', ylab='BART f(x)')
lines(c(-4, 4), c(-4, 4))

par(mfrow=c(2,2))

plot(range(xp),range(pf$yhat.test),xlab='x',ylab='f(x)',type='n')
lines(x,f(x),col='blue',lwd=2)
lines(xp,apply(pf$yhat.test,2,mean),col='red')
qpl = apply(pf$yhat.test,2,quantile,probs=c(.025,.975))
lines(xp,qpl[1,],col='green',lty=1)
lines(xp,qpl[2,],col='green',lty=1)
title(main='BART::lbart f(x) with 0.95 intervals')

plot(range(xp),range(mf$yhat.test),xlab='x',ylab='f(x)',type='n')
lines(x,f(x),col='blue',lwd=2)
lines(xp,apply(mf$yhat.test,2,mean),col='red')
qpl = apply(mf$yhat.test,2,quantile,probs=c(.025,.975))
lines(xp,qpl[1,],col='green',lty=1)
lines(xp,qpl[2,],col='green',lty=1)
title(main='BART::mc.lbart f(x) with 0.95 intervals')

plot(pf$yhat.test.mean,apply(mf$yhat.test,2,mean),xlab='BART::lbart',ylab='BART::mc.lbart')
abline(0,1,col='red')
title(main="BART::lbart f(x) vs. BART::mc.lbart f(x)")

## End(Not run)
set.seed(99)
n=5000
x = sort(-2+4*runif(n))
X=matrix(x,ncol=1)
f = function(x) {return((1/2)*x^3)}
FL = function(x) {return(exp(x)/(1+exp(x)))}
pv = FL(f(x))
y = rbinom(n,1,pv)
np=100
xp=-2+4*(1:np)/np
Xp=matrix(xp,ncol=1)

## parallel::mcparallel/mccollect do not exist on windows
## if(.Platform$OS.type=='unix') {
## ##test BART with token run to ensure installation works
##     mf = mc.lbart(X, y, nskip=5, ndpost=5, mc.cores=1, seed=99)
## }

## Not run: 
set.seed(99)
pf = lbart(X,y,Xp)

plot(f(Xp), pf$yhat.test.mean, xlim=c(-4, 4), ylim=c(-4, 4),
     xlab='True f(x)', ylab='BART f(x)')
lines(c(-4, 4), c(-4, 4))

mf = mc.lbart(X,y,Xp, mc.cores=4, seed=99)

plot(f(Xp), mf$yhat.test.mean, xlim=c(-4, 4), ylim=c(-4, 4),
     xlab='True f(x)', ylab='BART f(x)')
lines(c(-4, 4), c(-4, 4))

par(mfrow=c(2,2))

plot(range(xp),range(pf$yhat.test),xlab='x',ylab='f(x)',type='n')
lines(x,f(x),col='blue',lwd=2)
lines(xp,apply(pf$yhat.test,2,mean),col='red')
qpl = apply(pf$yhat.test,2,quantile,probs=c(.025,.975))
lines(xp,qpl[1,],col='green',lty=1)
lines(xp,qpl[2,],col='green',lty=1)
title(main='BART::lbart f(x) with 0.95 intervals')

plot(range(xp),range(mf$yhat.test),xlab='x',ylab='f(x)',type='n')
lines(x,f(x),col='blue',lwd=2)
lines(xp,apply(mf$yhat.test,2,mean),col='red')
qpl = apply(mf$yhat.test,2,quantile,probs=c(.025,.975))
lines(xp,qpl[1,],col='green',lty=1)
lines(xp,qpl[2,],col='green',lty=1)
title(main='BART::mc.lbart f(x) with 0.95 intervals')

plot(pf$yhat.test.mean,apply(mf$yhat.test,2,mean),xlab='BART::lbart',ylab='BART::mc.lbart')
abline(0,1,col='red')
title(main="BART::lbart f(x) vs. BART::mc.lbart f(x)")

## End(Not run)

Probit BART for dichotomous outcomes with Normal latents and parallel computation

Description

BART is a Bayesian “sum-of-trees” model.
For a binary response $y$ , $P(Y=1 | x) = F(f(x))$ , where $F$ denotes the standard normal cdf (probit link).

In both cases, $f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

mc.pbart(
   x.train, y.train, x.test=matrix(0.0,0,0),
   sparse=FALSE, theta=0, omega=1,
   a=0.5, b=1, augment=FALSE, rho=NULL,
   xinfo=matrix(0.0,0,0), usequants=FALSE,
   cont=FALSE, rm.const=TRUE,
   k=2.0, power=2.0, base=.95,
   binaryOffset=NULL,
   ntree=50L, numcut=100L,
   ndpost=1000L, nskip=100L,
   keepevery=1L, printevery=100,
   keeptrainfits=TRUE, transposed=FALSE,
   mc.cores = 2L, nice = 19L,
   seed = 99L
)
mc.pbart(
   x.train, y.train, x.test=matrix(0.0,0,0),
   sparse=FALSE, theta=0, omega=1,
   a=0.5, b=1, augment=FALSE, rho=NULL,
   xinfo=matrix(0.0,0,0), usequants=FALSE,
   cont=FALSE, rm.const=TRUE,
   k=2.0, power=2.0, base=.95,
   binaryOffset=NULL,
   ntree=50L, numcut=100L,
   ndpost=1000L, nskip=100L,
   keepevery=1L, printevery=100,
   keeptrainfits=TRUE, transposed=FALSE,
   mc.cores = 2L, nice = 19L,
   seed = 99L
)

Arguments

`x.train`	Explanatory variables for training (in sample) data. May be a matrix or a data frame, with (as usual) rows corresponding to observations and columns to variables. If a variable is a factor in a data frame, it is replaced with dummies. Note that q dummies are created if q>2 and one dummy is created if q=2, where q is the number of levels of the factor. `pbart` will generate draws of $f(x)$ for each $x$ which is a row of x.train.
`y.train`	Binary dependent variable for training (in sample) data.
`x.test`	Explanatory variables for test (out of sample) data. Should have same structure as x.train. `pbart` will generate draws of $f(x)$ for each $x$ which is a row of x.test.
`sparse`	Whether to perform variable selection based on a sparse Dirichlet prior rather than simply uniform; see Linero 2016.
`theta`	Set $theta$ parameter; zero means random.
`omega`	Set $omega$ parameter; zero means random.
`a`	Sparse parameter for $Beta(a, b)$ prior: $0.5<=a<=1$ where lower values inducing more sparsity.
`b`	Sparse parameter for $Beta(a, b)$ prior; typically, $b=1$ .
`rho`	Sparse parameter: typically $rho=p$ where $p$ is the number of covariates under consideration.
`augment`	Whether data augmentation is to be performed in sparse variable selection.
`xinfo`	You can provide the cutpoints to BART or let BART choose them for you. To provide them, use the `xinfo` argument to specify a list (matrix) where the items (rows) are the covariates and the contents of the items (columns) are the cutpoints.
`usequants`	If `usequants=FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, uniform quantiles are used for the cutpoints.
`cont`	Whether or not to assume all variables are continuous.
`rm.const`	Whether or not to remove constant variables.
`k`	For binary y, k is the number of prior standard deviations $f(x)$ is away from +/-3. The bigger k is, the more conservative the fitting will be.
`power`	Power parameter for tree prior.
`base`	Base parameter for tree prior.
`binaryOffset`	Used for binary $y$ . The model is $P(Y=1 \| x) = F(f(x) + binaryOffset)$ .
`ntree`	The number of trees in the sum.
`numcut`	The number of possible values of c (see usequants). If a single number if given, this is used for all variables. Otherwise a vector with length equal to ncol(x.train) is required, where the $i^{th}$ element gives the number of c used for the $i^{th}$ variable in x.train. If usequants is false, numcut equally spaced cutoffs are used covering the range of values in the corresponding column of x.train. If usequants is true, then min(numcut, the number of unique values in the corresponding columns of x.train - 1) c values are used.
`ndpost`	The number of posterior draws returned.
`nskip`	Number of MCMC iterations to be treated as burn in.
`keepevery`	Every keepevery draw is kept to be returned to the user.
`printevery`	As the MCMC runs, a message is printed every printevery draws.
`keeptrainfits`	Whether to keep `yhat.train` or not.
`transposed`	When running `pbart` in parallel, it is more memory-efficient to transpose `x.train` and `x.test`, if any, prior to calling `mc.pbart`.
`seed`	Setting the seed required for reproducible MCMC.
`mc.cores`	Number of cores to employ in parallel.
`nice`	Set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest).

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from $f$ in the binary $y$ case.

Value

mc.pbart returns an object of type pbart which is essentially a list.

`yhat.train`	A matrix with ndpost rows and nrow(x.train) columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to a row of x.train. The $(i,j)$ value is $f^(x)$ for the $i^{th}$ kept draw of $f$ and the $j^{th}$ row of x.train. Burn-in is dropped.
`yhat.test`	Same as yhat.train but now the x's are the rows of the test data.
`varcount`	a matrix with ndpost rows and nrow(x.train) columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times that variable is used in a tree decision rule (over all trees) is given.

In addition the list has a binaryOffset component giving the value used.

Note that in the binary $y$ , case yhat.train and yhat.test are $f(x)$ + binaryOffset. If you want draws of the probability $P(Y=1 | x)$ you need to apply the normal cdf (pnorm) to these values.

Examples

set.seed(99)
n=5000
x = sort(-2+4*runif(n))
X=matrix(x,ncol=1)
f = function(x) {return((1/2)*x^3)}
FL = function(x) {return(exp(x)/(1+exp(x)))}
pv = FL(f(x))
y = rbinom(n,1,pv)
np=100
xp=-2+4*(1:np)/np
Xp=matrix(xp,ncol=1)

## parallel::mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
    mf = mc.pbart(X, y, nskip=5, ndpost=5, mc.cores=1, seed=99)
}

## Not run: 
set.seed(99)
pf = pbart(X,y,Xp)

## plot(f(Xp), pf$yhat.test.mean, xlim=c(-4, 4), ylim=c(-4, 4),
##      xlab='True f(x)', ylab='BART f(x)')
## lines(c(-4, 4), c(-4, 4))

mf = mc.pbart(X,y,Xp, mc.cores=4, seed=99)

## plot(f(Xp), mf$yhat.test.mean, xlim=c(-4, 4), ylim=c(-4, 4),
##      xlab='True f(x)', ylab='BART f(x)')
## lines(c(-4, 4), c(-4, 4))

par(mfrow=c(2,2))

plot(range(xp),range(pf$yhat.test),xlab='x',ylab='f(x)',type='n')
lines(x,f(x),col='blue',lwd=2)
lines(xp,apply(pf$yhat.test,2,mean),col='red')
qpl = apply(pf$yhat.test,2,quantile,probs=c(.025,.975))
lines(xp,qpl[1,],col='green',lty=1)
lines(xp,qpl[2,],col='green',lty=1)
title(main='BART::pbart f(x) with 0.95 intervals')

plot(range(xp),range(mf$yhat.test),xlab='x',ylab='f(x)',type='n')
lines(x,f(x),col='blue',lwd=2)
lines(xp,apply(mf$yhat.test,2,mean),col='red')
qpl = apply(mf$yhat.test,2,quantile,probs=c(.025,.975))
lines(xp,qpl[1,],col='green',lty=1)
lines(xp,qpl[2,],col='green',lty=1)
title(main='BART::mc.pbart f(x) with 0.95 intervals')

## plot(pf$yhat.test.mean,apply(mf$yhat.test,2,mean),xlab='BART::pbart',ylab='BART::mc.pbart')
## abline(0,1,col='red')
## title(main="BART::pbart f(x) vs. BART::mc.pbart f(x)")

## End(Not run)
set.seed(99)
n=5000
x = sort(-2+4*runif(n))
X=matrix(x,ncol=1)
f = function(x) {return((1/2)*x^3)}
FL = function(x) {return(exp(x)/(1+exp(x)))}
pv = FL(f(x))
y = rbinom(n,1,pv)
np=100
xp=-2+4*(1:np)/np
Xp=matrix(xp,ncol=1)

## parallel::mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
    mf = mc.pbart(X, y, nskip=5, ndpost=5, mc.cores=1, seed=99)
}

## Not run: 
set.seed(99)
pf = pbart(X,y,Xp)

## plot(f(Xp), pf$yhat.test.mean, xlim=c(-4, 4), ylim=c(-4, 4),
##      xlab='True f(x)', ylab='BART f(x)')
## lines(c(-4, 4), c(-4, 4))

mf = mc.pbart(X,y,Xp, mc.cores=4, seed=99)

## plot(f(Xp), mf$yhat.test.mean, xlim=c(-4, 4), ylim=c(-4, 4),
##      xlab='True f(x)', ylab='BART f(x)')
## lines(c(-4, 4), c(-4, 4))

par(mfrow=c(2,2))

plot(range(xp),range(pf$yhat.test),xlab='x',ylab='f(x)',type='n')
lines(x,f(x),col='blue',lwd=2)
lines(xp,apply(pf$yhat.test,2,mean),col='red')
qpl = apply(pf$yhat.test,2,quantile,probs=c(.025,.975))
lines(xp,qpl[1,],col='green',lty=1)
lines(xp,qpl[2,],col='green',lty=1)
title(main='BART::pbart f(x) with 0.95 intervals')

plot(range(xp),range(mf$yhat.test),xlab='x',ylab='f(x)',type='n')
lines(x,f(x),col='blue',lwd=2)
lines(xp,apply(mf$yhat.test,2,mean),col='red')
qpl = apply(mf$yhat.test,2,quantile,probs=c(.025,.975))
lines(xp,qpl[1,],col='green',lty=1)
lines(xp,qpl[2,],col='green',lty=1)
title(main='BART::mc.pbart f(x) with 0.95 intervals')

## plot(pf$yhat.test.mean,apply(mf$yhat.test,2,mean),xlab='BART::pbart',ylab='BART::mc.pbart')
## abline(0,1,col='red')
## title(main="BART::pbart f(x) vs. BART::mc.pbart f(x)")

## End(Not run)

Predicting new observations with a previously fitted BART model

Description

BART is a Bayesian “sum-of-trees” model.
For a numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .

$f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

surv.pwbart(
                x.test,
                treedraws,
                binaryOffset=0,
                mc.cores=1L,
                type='pbart',
                transposed=FALSE, nice=19L
              )

mc.surv.pwbart(
                x.test,
                treedraws,
                binaryOffset=0,
                mc.cores=2L,
                type='pbart',
                transposed=FALSE, nice=19L
              )

mc.recur.pwbart(
                x.test,
                treedraws,
                binaryOffset=0,
                mc.cores=2L, 
                type='pbart',
                transposed=FALSE, nice=19L
               )

surv.pwbart(
                x.test,
                treedraws,
                binaryOffset=0,
                mc.cores=1L,
                type='pbart',
                transposed=FALSE, nice=19L
              )

mc.surv.pwbart(
                x.test,
                treedraws,
                binaryOffset=0,
                mc.cores=2L,
                type='pbart',
                transposed=FALSE, nice=19L
              )

mc.recur.pwbart(
                x.test,
                treedraws,
                binaryOffset=0,
                mc.cores=2L, 
                type='pbart',
                transposed=FALSE, nice=19L
               )

Arguments

`x.test`	Matrix of covariates to predict $y$ for.
`binaryOffset`	Mean to add on to $y$ prediction.
`treedraws`	`$treedraws` returned from `surv.bart`, `mc.surv.bart`, `recur.bart` or `mc.recur.bart`.
`mc.cores`	Number of threads to utilize.
`type`	Whether to employ Albert-Chib, `'pbart'`, or Holmes-Held, `'lbart'`.
`transposed`	When running `pwbart` or `mc.pwbart` in parallel, it is more memory-efficient to transpose `x.test` prior to calling the internal versions of these functions.
`nice`	Set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest).

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

Value

Returns an object of type survbart which is essentially a list with components:

`yhat.test`	A matrix with ndpost rows and nrow(x.test) columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to a row of x.train. The $(i,j)$ value is $f^(x)$ for the $i^{th}$ kept draw of $f$ and the $j^{th}$ row of x.train. Burn-in is dropped.
`surv.test`	test data fits for survival probability: not available for `mc.recur.pwbart`.
`surv.test.mean`	mean of `surv.test` over the posterior samples: not available for `mc.recur.pwbart`.
`haz.test`	test data fits for hazard: available for `mc.recur.pwbart` only.
`haz.test.mean`	mean of `haz.test` over the posterior samples: available for `mc.recur.pwbart` only.
`cum.test`	test data fits for cumulative hazard: available for `mc.recur.pwbart` only.
`cum.test.mean`	mean of `cum.test` over the posterior samples: available for `mc.recur.pwbart` only.

Examples


## load the advanced lung cancer example
data(lung)

group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno
times <- lung[group, 2]   ##lung$time
delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead
                          ##delta: 0=censored, 1=dead

## this study reports time in days rather than months like other studies
## coarsening from days to months will reduce the computational burden
times <- ceiling(times/30)

summary(times)
table(delta)

x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates

## lung$age:        Age in years
## lung$sex:        Male=1 Female=2
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)')

summary(x.train[ , 1])
table(x.train[ , 2])
table(x.train[ , 3])

x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios

dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

i <- 1

for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) {
    x.test[i, ] <- c(age, sex, ph.karno)
    i <- i+1
}

## this x.test is relatively small, but often you will want to
## predict for a large x.test matrix which may cause problems
## due to consumption of RAM so we can predict separately

## mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
    set.seed(99)
    post <- surv.bart(x.train=x.train, times=times, delta=delta, nskip=5, ndpost=5, keepevery=1)

    pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

    pred <- mc.surv.pwbart(pre$tx.test, post$treedraws, post$binaryOffset)
}

## Not run: 
## run one long MCMC chain in one process
set.seed(99)
post <- surv.bart(x.train=x.train, times=times, delta=delta)

## run "mc.cores" number of shorter MCMC chains in parallel processes
## post <- mc.surv.bart(x.train=x.train, times=times, delta=delta,
##                      mc.cores=8, seed=99)

pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

pred <- surv.pwbart(pre$tx.test, post$treedraws, post$binaryOffset)

## let's look at some survival curves
## first, a younger group with a healthier KPS
## age 50 with KPS=90: males and females
## males: row 17, females: row 23
x.test[c(17, 23), ]

low.risk.males <- 16*post$K+1:post$K ## K=unique times including censoring
low.risk.females <- 22*post$K+1:post$K

plot(post$times, pred$surv.test.mean[low.risk.males], type='s', col='blue',
     main='Age 50 with KPS=90', xlab='t', ylab='S(t)', ylim=c(0, 1))
points(post$times, pred$surv.test.mean[low.risk.females], type='s', col='red')


## End(Not run)
## load the advanced lung cancer example
data(lung)

group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno
times <- lung[group, 2]   ##lung$time
delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead
                          ##delta: 0=censored, 1=dead

## this study reports time in days rather than months like other studies
## coarsening from days to months will reduce the computational burden
times <- ceiling(times/30)

summary(times)
table(delta)

x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates

## lung$age:        Age in years
## lung$sex:        Male=1 Female=2
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)')

summary(x.train[ , 1])
table(x.train[ , 2])
table(x.train[ , 3])

x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios

dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

i <- 1

for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) {
    x.test[i, ] <- c(age, sex, ph.karno)
    i <- i+1
}

## this x.test is relatively small, but often you will want to
## predict for a large x.test matrix which may cause problems
## due to consumption of RAM so we can predict separately

## mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
    set.seed(99)
    post <- surv.bart(x.train=x.train, times=times, delta=delta, nskip=5, ndpost=5, keepevery=1)

    pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

    pred <- mc.surv.pwbart(pre$tx.test, post$treedraws, post$binaryOffset)
}

## Not run: 
## run one long MCMC chain in one process
set.seed(99)
post <- surv.bart(x.train=x.train, times=times, delta=delta)

## run "mc.cores" number of shorter MCMC chains in parallel processes
## post <- mc.surv.bart(x.train=x.train, times=times, delta=delta,
##                      mc.cores=8, seed=99)

pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

pred <- surv.pwbart(pre$tx.test, post$treedraws, post$binaryOffset)

## let's look at some survival curves
## first, a younger group with a healthier KPS
## age 50 with KPS=90: males and females
## males: row 17, females: row 23
x.test[c(17, 23), ]

low.risk.males <- 16*post$K+1:post$K ## K=unique times including censoring
low.risk.females <- 22*post$K+1:post$K

plot(post$times, pred$surv.test.mean[low.risk.males], type='s', col='blue',
     main='Age 50 with KPS=90', xlab='t', ylab='S(t)', ylim=c(0, 1))
points(post$times, pred$surv.test.mean[low.risk.females], type='s', col='red')


## End(Not run)

BART for continuous outcomes with parallel computation

Description

BART is a Bayesian “sum-of-trees” model.
For numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .

In both cases, $f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

mc.wbart(
   x.train, y.train, x.test=matrix(0.0,0,0),
   sparse=FALSE, theta=0, omega=1,
   a=0.5, b=1, augment=FALSE, rho=NULL,
   xinfo=matrix(0.0,0,0), usequants=FALSE,
   cont=FALSE, rm.const=TRUE,
   sigest=NA, sigdf=3, sigquant=0.90,
   k=2.0, power=2.0, base=.95,
   sigmaf=NA, lambda=NA, fmean=mean(y.train),
   w=rep(1,length(y.train)),
   ntree=200L, numcut=100L,
   ndpost=1000L, nskip=100L,
   keepevery=1L, printevery=100,
   keeptrainfits=TRUE, transposed=FALSE,
   
   mc.cores = 2L, nice = 19L,
   seed = 99L
)
mc.wbart(
   x.train, y.train, x.test=matrix(0.0,0,0),
   sparse=FALSE, theta=0, omega=1,
   a=0.5, b=1, augment=FALSE, rho=NULL,
   xinfo=matrix(0.0,0,0), usequants=FALSE,
   cont=FALSE, rm.const=TRUE,
   sigest=NA, sigdf=3, sigquant=0.90,
   k=2.0, power=2.0, base=.95,
   sigmaf=NA, lambda=NA, fmean=mean(y.train),
   w=rep(1,length(y.train)),
   ntree=200L, numcut=100L,
   ndpost=1000L, nskip=100L,
   keepevery=1L, printevery=100,
   keeptrainfits=TRUE, transposed=FALSE,
   
   mc.cores = 2L, nice = 19L,
   seed = 99L
)

Arguments

`x.train`	Explanatory variables for training (in sample) data. May be a matrix or a data frame, with (as usual) rows corresponding to observations and columns to variables. If a variable is a factor in a data frame, it is replaced with dummies. Note that q dummies are created if q>2 and one dummy is created if q=2, where q is the number of levels of the factor. `wbart` will generate draws of $f(x)$ for each $x$ which is a row of x.train.
`y.train`	Dependent variable for training (in sample) data. If y is numeric a continous response model is fit (normal errors).
`x.test`	Explanatory variables for test (out of sample) data. Should have same structure as x.train. `wbart` will generate draws of $f(x)$ for each $x$ which is a row of x.test.
`sparse`	Whether to perform variable selection based on a sparse Dirichlet prior rather than simply uniform; see Linero 2016.
`theta`	Set $theta$ parameter; zero means random.
`omega`	Set $omega$ parameter; zero means random.
`a`	Sparse parameter for $Beta(a, b)$ prior: $0.5<=a<=1$ where lower values inducing more sparsity.
`b`	Sparse parameter for $Beta(a, b)$ prior; typically, $b=1$ .
`rho`	Sparse parameter: typically $rho=p$ where $p$ is the number of covariates under consideration.
`augment`	Whether data augmentation is to be performed in sparse variable selection.
`xinfo`	You can provide the cutpoints to BART or let BART choose them for you. To provide them, use the `xinfo` argument to specify a list (matrix) where the items (rows) are the covariates and the contents of the items (columns) are the cutpoints.
`usequants`	If `usequants=FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, uniform quantiles are used for the cutpoints.
`cont`	Whether or not to assume all variables are continuous.
`rm.const`	Whether or not to remove constant variables.
`sigest`	The prior for the error variance ( $\sigma^2$ ) is inverted chi-squared (the standard conditionally conjugate prior). The prior is specified by choosing the degrees of freedom, a rough estimate of the corresponding standard deviation and a quantile to put this rough estimate at. If sigest=NA then the rough estimate will be the usual least squares estimator. Otherwise the supplied value will be used.
`sigdf`	Degrees of freedom for error variance prior.
`sigquant`	The quantile of the prior that the rough estimate (see sigest) is placed at. The closer the quantile is to 1, the more aggresive the fit will be as you are putting more prior weight on error standard deviations ( $\sigma$ ) less than the rough estimate.
`k`	For numeric y, k is the number of prior standard deviations $E(Y\|x) = f(x)$ is away from +/-.5. The bigger k is, the more conservative the fitting will be.
`power`	Power parameter for tree prior.
`base`	Base parameter for tree prior.
`sigmaf`	The SD of f.
`lambda`	The scale of the prior for the variance.
`fmean`	BART operates on `y.train` centered by `fmean`.
`w`	Vector of weights which multiply the variance.
`ntree`	The number of trees in the sum.
`numcut`	The number of possible values of c (see usequants). If a single number if given, this is used for all variables. Otherwise a vector with length equal to ncol(x.train) is required, where the $i^{th}$ element gives the number of c used for the $i^{th}$ variable in x.train. If usequants is false, numcut equally spaced cutoffs are used covering the range of values in the corresponding column of x.train. If usequants is true, then min(numcut, the number of unique values in the corresponding columns of x.train - 1) c values are used.
`ndpost`	The number of posterior draws returned.
`nskip`	Number of MCMC iterations to be treated as burn in.
`keepevery`	Every keepevery draw is kept to be returned to the user.
`printevery`	As the MCMC runs, a message is printed every printevery draws.
`keeptrainfits`	Whether to keep `yhat.train` or not.
`transposed`	When running `wbart` in parallel, it is more memory-efficient to transpose `x.train` and `x.test`, if any, prior to calling `mc.wbart`.
`seed`	Setting the seed required for reproducible MCMC.
`mc.cores`	Number of cores to employ in parallel.
`nice`	Set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest).

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case.

Value

mc.wbart returns an object of type wbart which is essentially a list.

`yhat.train`	A matrix with ndpost rows and nrow(x.train) columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to a row of x.train. The $(i,j)$ value is $f^(x)$ for the $i^{th}$ kept draw of $f$ and the $j^{th}$ row of x.train. Burn-in is dropped.
`yhat.test`	Same as yhat.train but now the x's are the rows of the test data.
`yhat.train.mean`	train data fits = mean of yhat.train columns.
`yhat.test.mean`	test data fits = mean of yhat.test columns.
`varcount`	a matrix with ndpost rows and nrow(x.train) columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times that variable is used in a tree decision rule (over all trees) is given.

Examples

##simulate data (example from Friedman MARS paper)
f = function(x){
10*sin(pi*x[,1]*x[,2]) + 20*(x[,3]-.5)^2+10*x[,4]+5*x[,5]
}
sigma = 1.0  #y = f(x) + sigma*z , z~N(0,1)
n = 100      #number of observations
set.seed(99)
x=matrix(runif(n*10),n,10) #10 variables, only first 5 matter
Ey = f(x)
y=Ey+sigma*rnorm(n)
lmFit = lm(y~.,data.frame(x,y)) #compare lm fit to BART later

## parallel::mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
    bartFit = mc.wbart(x,y,mc.cores=2,seed=99,nskip=5,ndpost=5)
}

## Not run: 
##run BART
bartFit = mc.wbart(x,y,mc.cores=5,seed=99)
##compare BART fit to linear matter and truth = Ey
fitmat = cbind(y,Ey,lmFit$fitted,bartFit$yhat.train.mean)
colnames(fitmat) = c('y','Ey','lm','bart')
print(cor(fitmat))

## End(Not run)
##simulate data (example from Friedman MARS paper)
f = function(x){
10*sin(pi*x[,1]*x[,2]) + 20*(x[,3]-.5)^2+10*x[,4]+5*x[,5]
}
sigma = 1.0  #y = f(x) + sigma*z , z~N(0,1)
n = 100      #number of observations
set.seed(99)
x=matrix(runif(n*10),n,10) #10 variables, only first 5 matter
Ey = f(x)
y=Ey+sigma*rnorm(n)
lmFit = lm(y~.,data.frame(x,y)) #compare lm fit to BART later

## parallel::mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
    bartFit = mc.wbart(x,y,mc.cores=2,seed=99,nskip=5,ndpost=5)
}

## Not run: 
##run BART
bartFit = mc.wbart(x,y,mc.cores=5,seed=99)
##compare BART fit to linear matter and truth = Ey
fitmat = cbind(y,Ey,lmFit$fitted,bartFit$yhat.train.mean)
colnames(fitmat) = c('y','Ey','lm','bart')
print(cor(fitmat))

## End(Not run)

Global SE variable selection for BART with parallel computation

Description

Here we implement the global SE method for variable selection in nonparametric survival analysis with BART. Unfortunately, the method is very computationally intensive so we present some trade-offs below.

Usage

mc.wbart.gse( x.train, y.train,
              P=50L, R=5L, ntree=20L, numcut=100L, C=1, alpha=0.05,
              k=2.0, power=2.0, base=0.95,
              ndpost=2000L, nskip=100L,
              printevery=100L, keepevery=1L, keeptrainfits=FALSE,
              seed=99L, mc.cores=2L, nice=19L 
              )
mc.wbart.gse( x.train, y.train,
              P=50L, R=5L, ntree=20L, numcut=100L, C=1, alpha=0.05,
              k=2.0, power=2.0, base=0.95,
              ndpost=2000L, nskip=100L,
              printevery=100L, keepevery=1L, keeptrainfits=FALSE,
              seed=99L, mc.cores=2L, nice=19L 
              )

Arguments

`x.train`	Explanatory variables for training (in sample) data. Must be a matrix with (as usual) rows corresponding to observations and columns to variables. `surv.bart` will generate draws of $f(t, x)$ for each $x$ which is a row of x.train.
`y.train`	The continuous outcome.
`P`	The number of permutations: typically 50 or 100.
`R`	The number of replicates: typically 5 or 10.
`ntree`	The number of trees. In variable selection, the number of trees is smaller than what might be used for the best fit.
`numcut`	The number of possible values of c (see usequants). If a single number if given, this is used for all variables. Otherwise a vector with length equal to ncol(x.train) is required, where the $i^{th}$ element gives the number of c used for the $i^{th}$ variable in x.train. If usequants is false, numcut equally spaced cutoffs are used covering the range of values in the corresponding column of x.train. If usequants is true, then min(numcut, the number of unique values in the corresponding columns of x.train - 1) c values are used.
`C`	The starting value for the multiple of SE. You should not need to change this except in rare circumstances.
`alpha`	The global SE method relies on simultaneous 1-`alpha` coverage across the permutations for all predictor variables.
`k`	k is the number of prior standard deviations $f(t, x)$ is away from +/-3. The bigger k is, the more conservative the fitting will be.
`power`	Power parameter for tree prior.
`base`	Base parameter for tree prior.
`ndpost`	The number of posterior draws after burn in. In the global SE method, generally, the method is repeated several times to establish the variable count probabilities. However, we take the alternative approach of simply running the MCMC chain longer which should result in the same stabilization of the estimates. Therefore, the number of posterior draws in variable selection should be set to a larger value than would be typically anticipated for fitting.
`nskip`	Number of MCMC iterations to be treated as burn in.
`printevery`	As the MCMC runs, a message is printed every printevery draws.
`keepevery`	Every `keepevery` draw is kept.
`keeptrainfits`	If `TRUE` the draws of $f(t, x)$ for $x$ = rows of x.train are generated.
`seed`	`seed` required for reproducible MCMC.
`mc.cores`	Number of cores to employ in parallel.
`nice`	Set the job priority. The default priority is 19: priorities go from 0 (highest) to 19 (lowest).

Value

mc.wbart.gse returns a list.

References

Bleich, J., Kapelner, A., George, E.I., and Jensen, S.T. (2014). Variable selection for BART: an application to gene regulation. The Annals of Applied Statistics, 8:1750-81.

Examples

## Not run: 

library(ElemStatLearn)

data(phoneme)

x.train <- matrix(NA, nrow=4509, ncol=257)
    
dimnames(x.train)[[2]] <- c(paste0('x.', 1:256), 'speaker')
    
x.train[ , 257] <- as.numeric(phoneme$speaker)

for(j in 1:256) x.train[ , j] <- as.numeric(phoneme[ , paste0('x.', j)])

gse <- mc.wbart.gse(x.train, as.numeric(phoneme$g), mc.cores=5, seed=99)

## important variables
dimnames(x.train)[[2]][gse$which]
    

## End(Not run)
## Not run: 

library(ElemStatLearn)

data(phoneme)

x.train <- matrix(NA, nrow=4509, ncol=257)
    
dimnames(x.train)[[2]] <- c(paste0('x.', 1:256), 'speaker')
    
x.train[ , 257] <- as.numeric(phoneme$speaker)

for(j in 1:256) x.train[ , j] <- as.numeric(phoneme[ , paste0('x.', j)])

gse <- mc.wbart.gse(x.train, as.numeric(phoneme$g), mc.cores=5, seed=99)

## important variables
dimnames(x.train)[[2]][gse$which]
    

## End(Not run)

Probit BART for dichotomous outcomes with Normal latents

Description

BART is a Bayesian “sum-of-trees” model.
For a binary response $y$ , $P(Y=1 | x) = F(f(x))$ , where $F$ denotes the standard Normal CDF (probit link).

In both cases, $f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

pbart(
   x.train, y.train, x.test=matrix(0.0,0,0),
   sparse=FALSE, theta=0, omega=1,
   a=0.5, b=1, augment=FALSE, rho=NULL,
   xinfo=matrix(0.0,0,0), usequants=FALSE,
   cont=FALSE, rm.const=TRUE,
   k=2.0, power=2.0, base=.95,
   binaryOffset=NULL, 
   ntree=50L, numcut=100L,
   ndpost=1000L, nskip=100L, keepevery=1L,
   nkeeptrain=ndpost, nkeeptest=ndpost,
   nkeeptreedraws=ndpost,
   printevery=100L, transposed=FALSE 
)
pbart(
   x.train, y.train, x.test=matrix(0.0,0,0),
   sparse=FALSE, theta=0, omega=1,
   a=0.5, b=1, augment=FALSE, rho=NULL,
   xinfo=matrix(0.0,0,0), usequants=FALSE,
   cont=FALSE, rm.const=TRUE,
   k=2.0, power=2.0, base=.95,
   binaryOffset=NULL, 
   ntree=50L, numcut=100L,
   ndpost=1000L, nskip=100L, keepevery=1L,
   nkeeptrain=ndpost, nkeeptest=ndpost,
   nkeeptreedraws=ndpost,
   printevery=100L, transposed=FALSE 
)

Arguments

`x.train`	Explanatory variables for training (in sample) data. May be a matrix or a data frame, with (as usual) rows corresponding to observations and columns to variables. If a variable is a factor in a data frame, it is replaced with dummies. Note that q dummies are created if q>2 and one dummy is created if q=2, where q is the number of levels of the factor. `pbart` will generate draws of $f(x)$ for each $x$ which is a row of x.train.
`y.train`	Binary dependent variable for training (in sample) data.
`x.test`	Explanatory variables for test (out of sample) data. Should have same structure as x.train. `pbart` will generate draws of $f(x)$ for each $x$ which is a row of x.test.
`sparse`	Whether to perform variable selection based on a sparse Dirichlet prior rather than simply uniform; see Linero 2016.
`theta`	Set $theta$ parameter; zero means random.
`omega`	Set $omega$ parameter; zero means random.
`a`	Sparse parameter for $Beta(a, b)$ prior: $0.5<=a<=1$ where lower values inducing more sparsity.
`b`	Sparse parameter for $Beta(a, b)$ prior; typically, $b=1$ .
`rho`	Sparse parameter: typically $rho=p$ where $p$ is the number of covariates under consideration.
`augment`	Whether data augmentation is to be performed in sparse variable selection.
`xinfo`	You can provide the cutpoints to BART or let BART choose them for you. To provide them, use the `xinfo` argument to specify a list (matrix) where the items (rows) are the covariates and the contents of the items (columns) are the cutpoints.
`usequants`	If `usequants=FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, uniform quantiles are used for the cutpoints.
`cont`	Whether or not to assume all variables are continuous.
`rm.const`	Whether or not to remove constant variables.
`k`	For binary y, k is the number of prior standard deviations $f(x)$ is away from +/-3. The bigger k is, the more conservative the fitting will be.
`power`	Power parameter for tree prior.
`base`	Base parameter for tree prior.
`binaryOffset`	Used for binary $y$ . The model is $P(Y=1 \| x) = F(f(x) + binaryOffset)$ .
`ntree`	The number of trees in the sum.
`numcut`	The number of possible values of c (see usequants). If a single number if given, this is used for all variables. Otherwise a vector with length equal to ncol(x.train) is required, where the $i^{th}$ element gives the number of c used for the $i^{th}$ variable in x.train. If usequants is false, numcut equally spaced cutoffs are used covering the range of values in the corresponding column of x.train. If usequants is true, then min(numcut, the number of unique values in the corresponding columns of x.train - 1) c values are used.
`ndpost`	The number of posterior draws returned.
`nskip`	Number of MCMC iterations to be treated as burn in.
`nkeeptrain`	Number of MCMC iterations to be returned for train data.
`nkeeptest`	Number of MCMC iterations to be returned for test data.
`nkeeptreedraws`	Number of MCMC iterations to be returned for tree draws.
`keepevery`	Every keepevery draw is kept to be returned to the user.
`printevery`	As the MCMC runs, a message is printed every printevery draws.
`transposed`	When running `pbart` in parallel, it is more memory-efficient to transpose `x.train` and `x.test`, if any, prior to calling `mc.pbart`.

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from $f$ in the binary $y$ case.

Value

pbart returns an object of type pbart which is essentially a list.

`yhat.train`	A matrix with ndpost rows and nrow(x.train) columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to a row of x.train. The $(i,j)$ value is $f^(x)$ for the $i^{th}$ kept draw of $f$ and the $j^{th}$ row of x.train. Burn-in is dropped.
`yhat.test`	Same as yhat.train but now the x's are the rows of the test data.
`varcount`	a matrix with ndpost rows and nrow(x.train) columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times that variable is used in a tree decision rule (over all trees) is given.

In addition the list has a binaryOffset component giving the value used.

Note that in the binary $y$ , case yhat.train and yhat.test are $f(x)$ + binaryOffset. If you want draws of the probability $P(Y=1 | x)$ you need to apply the Normal CDF (pnorm) to these values.

Examples


data(ACTG175)

## exclude those who do not have CD4 count at 96 weeks
ex <- is.na(ACTG175$cd496)
table(ex)

## inclusion criteria are CD4 counts between 200 and 500
ACTG175$cd40 <- min(500, max(250, ACTG175$cd40))

## calculate relative CD4 decline
y <- ((ACTG175$cd496-ACTG175$cd40)/ACTG175$cd40)[!ex]
summary(y)

## 0=failure, 1=success
y <- 1*(y > -0.5)

## summarize CD4 outcomes
table(y, ACTG175$arms[!ex])

table(y, ACTG175$arms[!ex])/
    matrix(table(ACTG175$arms[!ex]), nrow=2, ncol=4, byrow=TRUE)

## drop unneeded and unwanted variables
## 1: 'pidnum' patient ID number
##14: 'str2' which will be handled by strat1 below
##15: 'strat' which will be handled by strat1-strat3 below
##17: 'treat' handled by arm0-arm3 below
##18: 'offtrt' indicator of off-treatment before 96 weeks
##20: 'cd420' CD4 T cell count at 20 weeks
##21: 'cd496' CD4 T cell count at 96 weeks
##22: 'r' missing CD4 T cell count at 96 weeks
##24: 'cd820' CD8 T cell count at 20 weeks
##25: 'cens' indicator of observing the event in days
##26: 'days' number of days until the primary endpoint
##27: 'arms' handled by arm0-arm3 below
train <- as.matrix(ACTG175)[!ex, -c(1, 14:15, 17, 18, 20:22, 24:27)]
train <- cbind(1*(ACTG175$strat[!ex]==1), 1*(ACTG175$strat[!ex]==2),
               1*(ACTG175$strat[!ex]==3), train)
dimnames(train)[[2]][1:3] <- paste0('strat', 1:3)
train <- cbind(1*(ACTG175$arms[!ex]==0), 1*(ACTG175$arms[!ex]==1),
               1*(ACTG175$arms[!ex]==2), 1*(ACTG175$arms[!ex]==3), train)
dimnames(train)[[2]][1:4] <- paste0('arm', 0:3)

N <- nrow(train)

test0 <- train; test0[ , 1:4] <- 0; test0[ , 1] <- 1
test1 <- train; test1[ , 1:4] <- 0; test1[ , 2] <- 1
test2 <- train; test2[ , 1:4] <- 0; test2[ , 3] <- 1
test3 <- train; test3[ , 1:4] <- 0; test3[ , 4] <- 1

test <- rbind(test0, test1, test2, test3)

##test BART with token run to ensure installation works
set.seed(21)
post <- pbart(train, y, test, nskip=5, ndpost=5)

## Not run: 
set.seed(21)
post <- pbart(train, y, test)

## turn z-scores into probabilities
post$prob.test <- pnorm(post$yhat.test)

## average over the posterior samples
post$prob.test.mean <- apply(post$prob.test, 2, mean)

## place estimates for arms 0-3 next to each other for convenience
itr <- cbind(post$prob.test.mean[(1:N)], post$prob.test.mean[N+(1:N)],
             post$prob.test.mean[2*N+(1:N)], post$prob.test.mean[3*N+(1:N)])

## find the BART ITR for each patient
itr.pick <- integer(N)
for(i in 1:N) itr.pick[i] <- which(itr[i, ]==max(itr[i, ]))-1

## arms 0 and 3 (monotherapy) are never chosen
table(itr.pick)

## do arms 1 and 2 show treatment heterogeneity?
diff. <- apply(post$prob.test[ , 2*N+(1:N)]-post$prob.test[ , N+(1:N)], 2, mean)
plot(sort(diff.), type='h', main='ACTG175 trial: 50% CD4 decline from baseline at 96 weeks',
     xlab='Arm 2 (1) Preferable to the Right (Left)', ylab='Prob.Diff.: Arms 2 - 1')

library(rpart)
library(rpart.plot)

## make data frame for nicer names in the plot
var <- as.data.frame(train[ , -(1:4)])

dss <- rpart(diff. ~ var$age+var$gender+var$race+var$wtkg+var$cd40+var$cd80+
                   var$karnof+var$symptom+var$hemo+var$homo+var$drugs+var$z30+
                   var$zprior+var$oprior+var$strat1+var$strat2+var$strat3,
               method='anova', control=rpart.control(cp=0.1))
rpart.plot(dss, type=3, extra=101)

## if strat1==1 (antiretroviral naive), then arm 2 is better
## otherwise, arm 1
print(dss)

all0 <- apply(post$prob.test[ , (1:N)], 1, mean)
all1 <- apply(post$prob.test[ , N+(1:N)], 1, mean)
all2 <- apply(post$prob.test[ , 2*N+(1:N)], 1, mean)
all3 <- apply(post$prob.test[ , 3*N+(1:N)], 1, mean)

## BART ITR
BART.itr <- apply(post$prob.test[ , c(N+which(itr.pick==1), 2*N+which(itr.pick==2))], 1, mean)

test <- train
test[ , 1:4] <- 0
test[test[ , 5]==0, 2] <- 1
test[test[ , 5]==1, 3] <- 1

## BART ITR simple
BART.itr.simp <- pwbart(test, post$treedraws)
BART.itr.simp <- apply(pnorm(BART.itr.simp), 1, mean)

plot(density(BART.itr), xlab='Value', xlim=c(0.475, 0.775), lwd=2,
     main='ACTG175 trial: 50% CD4 decline from baseline at 96 weeks')
lines(density(BART.itr.simp), col='brown', lwd=2)
lines(density(all0), col='green', lwd=2)
lines(density(all1), col='red', lwd=2)
lines(density(all2), col='blue', lwd=2)
lines(density(all3), col='yellow', lwd=2)
legend('topleft', legend=c('All Arm 0 (ZDV only)', 'All Arm 1 (ZDV+DDI)',
                           'All Arm 2 (ZDV+DDC)', 'All Arm 3 (DDI only)',
                           'BART ITR simple', 'BART ITR'),
       col=c('green', 'red', 'blue', 'yellow', 'brown', 'black'), lty=1, lwd=2)


## End(Not run)
data(ACTG175)

## exclude those who do not have CD4 count at 96 weeks
ex <- is.na(ACTG175$cd496)
table(ex)

## inclusion criteria are CD4 counts between 200 and 500
ACTG175$cd40 <- min(500, max(250, ACTG175$cd40))

## calculate relative CD4 decline
y <- ((ACTG175$cd496-ACTG175$cd40)/ACTG175$cd40)[!ex]
summary(y)

## 0=failure, 1=success
y <- 1*(y > -0.5)

## summarize CD4 outcomes
table(y, ACTG175$arms[!ex])

table(y, ACTG175$arms[!ex])/
    matrix(table(ACTG175$arms[!ex]), nrow=2, ncol=4, byrow=TRUE)

## drop unneeded and unwanted variables
## 1: 'pidnum' patient ID number
##14: 'str2' which will be handled by strat1 below
##15: 'strat' which will be handled by strat1-strat3 below
##17: 'treat' handled by arm0-arm3 below
##18: 'offtrt' indicator of off-treatment before 96 weeks
##20: 'cd420' CD4 T cell count at 20 weeks
##21: 'cd496' CD4 T cell count at 96 weeks
##22: 'r' missing CD4 T cell count at 96 weeks
##24: 'cd820' CD8 T cell count at 20 weeks
##25: 'cens' indicator of observing the event in days
##26: 'days' number of days until the primary endpoint
##27: 'arms' handled by arm0-arm3 below
train <- as.matrix(ACTG175)[!ex, -c(1, 14:15, 17, 18, 20:22, 24:27)]
train <- cbind(1*(ACTG175$strat[!ex]==1), 1*(ACTG175$strat[!ex]==2),
               1*(ACTG175$strat[!ex]==3), train)
dimnames(train)[[2]][1:3] <- paste0('strat', 1:3)
train <- cbind(1*(ACTG175$arms[!ex]==0), 1*(ACTG175$arms[!ex]==1),
               1*(ACTG175$arms[!ex]==2), 1*(ACTG175$arms[!ex]==3), train)
dimnames(train)[[2]][1:4] <- paste0('arm', 0:3)

N <- nrow(train)

test0 <- train; test0[ , 1:4] <- 0; test0[ , 1] <- 1
test1 <- train; test1[ , 1:4] <- 0; test1[ , 2] <- 1
test2 <- train; test2[ , 1:4] <- 0; test2[ , 3] <- 1
test3 <- train; test3[ , 1:4] <- 0; test3[ , 4] <- 1

test <- rbind(test0, test1, test2, test3)

##test BART with token run to ensure installation works
set.seed(21)
post <- pbart(train, y, test, nskip=5, ndpost=5)

## Not run: 
set.seed(21)
post <- pbart(train, y, test)

## turn z-scores into probabilities
post$prob.test <- pnorm(post$yhat.test)

## average over the posterior samples
post$prob.test.mean <- apply(post$prob.test, 2, mean)

## place estimates for arms 0-3 next to each other for convenience
itr <- cbind(post$prob.test.mean[(1:N)], post$prob.test.mean[N+(1:N)],
             post$prob.test.mean[2*N+(1:N)], post$prob.test.mean[3*N+(1:N)])

## find the BART ITR for each patient
itr.pick <- integer(N)
for(i in 1:N) itr.pick[i] <- which(itr[i, ]==max(itr[i, ]))-1

## arms 0 and 3 (monotherapy) are never chosen
table(itr.pick)

## do arms 1 and 2 show treatment heterogeneity?
diff. <- apply(post$prob.test[ , 2*N+(1:N)]-post$prob.test[ , N+(1:N)], 2, mean)
plot(sort(diff.), type='h', main='ACTG175 trial: 50% CD4 decline from baseline at 96 weeks',
     xlab='Arm 2 (1) Preferable to the Right (Left)', ylab='Prob.Diff.: Arms 2 - 1')

library(rpart)
library(rpart.plot)

## make data frame for nicer names in the plot
var <- as.data.frame(train[ , -(1:4)])

dss <- rpart(diff. ~ var$age+var$gender+var$race+var$wtkg+var$cd40+var$cd80+
                   var$karnof+var$symptom+var$hemo+var$homo+var$drugs+var$z30+
                   var$zprior+var$oprior+var$strat1+var$strat2+var$strat3,
               method='anova', control=rpart.control(cp=0.1))
rpart.plot(dss, type=3, extra=101)

## if strat1==1 (antiretroviral naive), then arm 2 is better
## otherwise, arm 1
print(dss)

all0 <- apply(post$prob.test[ , (1:N)], 1, mean)
all1 <- apply(post$prob.test[ , N+(1:N)], 1, mean)
all2 <- apply(post$prob.test[ , 2*N+(1:N)], 1, mean)
all3 <- apply(post$prob.test[ , 3*N+(1:N)], 1, mean)

## BART ITR
BART.itr <- apply(post$prob.test[ , c(N+which(itr.pick==1), 2*N+which(itr.pick==2))], 1, mean)

test <- train
test[ , 1:4] <- 0
test[test[ , 5]==0, 2] <- 1
test[test[ , 5]==1, 3] <- 1

## BART ITR simple
BART.itr.simp <- pwbart(test, post$treedraws)
BART.itr.simp <- apply(pnorm(BART.itr.simp), 1, mean)

plot(density(BART.itr), xlab='Value', xlim=c(0.475, 0.775), lwd=2,
     main='ACTG175 trial: 50% CD4 decline from baseline at 96 weeks')
lines(density(BART.itr.simp), col='brown', lwd=2)
lines(density(all0), col='green', lwd=2)
lines(density(all1), col='red', lwd=2)
lines(density(all2), col='blue', lwd=2)
lines(density(all3), col='yellow', lwd=2)
legend('topleft', legend=c('All Arm 0 (ZDV only)', 'All Arm 1 (ZDV+DDI)',
                           'All Arm 2 (ZDV+DDC)', 'All Arm 3 (DDI only)',
                           'BART ITR simple', 'BART ITR'),
       col=c('green', 'red', 'blue', 'yellow', 'brown', 'black'), lty=1, lwd=2)


## End(Not run)

Predicting new observations with a previously fitted BART model

Description

BART is a Bayesian “sum-of-trees” model.
For a numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .

$f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

## S3 method for class 'crisk2bart'
predict(object, newdata, newdata2, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)
## S3 method for class 'crisk2bart'
predict(object, newdata, newdata2, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)

Arguments

`object`	`object` returned from previous BART fit with `crisk2.bart` or `mc.crisk2.bart`.
`newdata`	Matrix of covariates to predict the distribution of $t1$ .
`newdata2`	Matrix of covariates to predict the distribution of $t2$ .
`mc.cores`	Number of threads to utilize.
`openmp`	Logical value dictating whether OpenMP is utilized for parallel processing. Of course, this depends on whether OpenMP is available on your system which, by default, is verified with `mc.cores.openmp`.
`...`	Other arguments which will be passed on to `pwbart`.

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

Value

Returns an object of type crisk2bart with predictions corresponding to newdata and newdata2.

Examples


data(transplant)

delta <- (as.numeric(transplant$event)-1)
## recode so that delta=1 is cause of interest; delta=2 otherwise
delta[delta==1] <- 4
delta[delta==2] <- 1
delta[delta>1] <- 2
table(delta, transplant$event)

times <- pmax(1, ceiling(transplant$futime/7)) ## weeks
##times <- pmax(1, ceiling(transplant$futime/30.5)) ## months
table(times)

typeO <- 1*(transplant$abo=='O')
typeA <- 1*(transplant$abo=='A')
typeB <- 1*(transplant$abo=='B')
typeAB <- 1*(transplant$abo=='AB')
table(typeA, typeO)

x.train <- cbind(typeO, typeA, typeB, typeAB)

x.test <- cbind(1, 0, 0, 0)
dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

## parallel::mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
        post <- mc.crisk2.bart(x.train=x.train, times=times, delta=delta,
                               seed=99, mc.cores=2, nskip=5, ndpost=5,
                               keepevery=1)

        pre <- surv.pre.bart(x.train=x.train, x.test=x.test,
                             times=times, delta=delta)

        K <- post$K

        pred <- mc.crisk2.pwbart(pre$tx.test, pre$tx.test,
                                post$treedraws, post$treedraws2,
                                post$binaryOffset, post$binaryOffset2)
}

## Not run: 

## run one long MCMC chain in one process
## set.seed(99)
## post <- crisk2.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

## in the interest of time, consider speeding it up by parallel processing
## run "mc.cores" number of shorter MCMC chains in parallel processes
post <- mc.crisk2.bart(x.train=x.train,
                       times=times, delta=delta,
                       x.test=x.test, seed=99, mc.cores=8)

## check <- mc.crisk2.pwbart(post$tx.test, post$tx.test,
##                           post$treedraws, post$treedraws2,
##                           post$binaryOffset,
##                           post$binaryOffset2, mc.cores=8)
check <- predict(post, newdata=post$tx.test, newdata2=post$tx.test2,
                 mc.cores=8)

print(c(post$surv.test.mean[1], check$surv.test.mean[1],
        post$surv.test.mean[1]-check$surv.test.mean[1]), digits=22)

print(all(round(post$surv.test.mean, digits=9)==
    round(check$surv.test.mean, digits=9)))

print(c(post$cif.test.mean[1], check$cif.test.mean[1],
        post$cif.test.mean[1]-check$cif.test.mean[1]), digits=22)

print(all(round(post$cif.test.mean, digits=9)==
    round(check$cif.test.mean, digits=9)))

print(c(post$cif.test2.mean[1], check$cif.test2.mean[1],
        post$cif.test2.mean[1]-check$cif.test2.mean[1]), digits=22)

print(all(round(post$cif.test2.mean, digits=9)==
    round(check$cif.test2.mean, digits=9)))


## End(Not run)
data(transplant)

delta <- (as.numeric(transplant$event)-1)
## recode so that delta=1 is cause of interest; delta=2 otherwise
delta[delta==1] <- 4
delta[delta==2] <- 1
delta[delta>1] <- 2
table(delta, transplant$event)

times <- pmax(1, ceiling(transplant$futime/7)) ## weeks
##times <- pmax(1, ceiling(transplant$futime/30.5)) ## months
table(times)

typeO <- 1*(transplant$abo=='O')
typeA <- 1*(transplant$abo=='A')
typeB <- 1*(transplant$abo=='B')
typeAB <- 1*(transplant$abo=='AB')
table(typeA, typeO)

x.train <- cbind(typeO, typeA, typeB, typeAB)

x.test <- cbind(1, 0, 0, 0)
dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

## parallel::mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
        post <- mc.crisk2.bart(x.train=x.train, times=times, delta=delta,
                               seed=99, mc.cores=2, nskip=5, ndpost=5,
                               keepevery=1)

        pre <- surv.pre.bart(x.train=x.train, x.test=x.test,
                             times=times, delta=delta)

        K <- post$K

        pred <- mc.crisk2.pwbart(pre$tx.test, pre$tx.test,
                                post$treedraws, post$treedraws2,
                                post$binaryOffset, post$binaryOffset2)
}

## Not run: 

## run one long MCMC chain in one process
## set.seed(99)
## post <- crisk2.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

## in the interest of time, consider speeding it up by parallel processing
## run "mc.cores" number of shorter MCMC chains in parallel processes
post <- mc.crisk2.bart(x.train=x.train,
                       times=times, delta=delta,
                       x.test=x.test, seed=99, mc.cores=8)

## check <- mc.crisk2.pwbart(post$tx.test, post$tx.test,
##                           post$treedraws, post$treedraws2,
##                           post$binaryOffset,
##                           post$binaryOffset2, mc.cores=8)
check <- predict(post, newdata=post$tx.test, newdata2=post$tx.test2,
                 mc.cores=8)

print(c(post$surv.test.mean[1], check$surv.test.mean[1],
        post$surv.test.mean[1]-check$surv.test.mean[1]), digits=22)

print(all(round(post$surv.test.mean, digits=9)==
    round(check$surv.test.mean, digits=9)))

print(c(post$cif.test.mean[1], check$cif.test.mean[1],
        post$cif.test.mean[1]-check$cif.test.mean[1]), digits=22)

print(all(round(post$cif.test.mean, digits=9)==
    round(check$cif.test.mean, digits=9)))

print(c(post$cif.test2.mean[1], check$cif.test2.mean[1],
        post$cif.test2.mean[1]-check$cif.test2.mean[1]), digits=22)

print(all(round(post$cif.test2.mean, digits=9)==
    round(check$cif.test2.mean, digits=9)))


## End(Not run)

Predicting new observations with a previously fitted BART model

Description

BART is a Bayesian “sum-of-trees” model.
For a numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .

$f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

## S3 method for class 'criskbart'
predict(object, newdata, newdata2, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)
## S3 method for class 'criskbart'
predict(object, newdata, newdata2, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)

Arguments

`object`	`object` returned from previous BART fit with `crisk.bart` or `mc.crisk.bart`.
`newdata`	Matrix of covariates to predict the distribution of $t1$ .
`newdata2`	Matrix of covariates to predict the distribution of $t2$ .
`mc.cores`	Number of threads to utilize.
`openmp`	Logical value dictating whether OpenMP is utilized for parallel processing. Of course, this depends on whether OpenMP is available on your system which, by default, is verified with `mc.cores.openmp`.
`...`	Other arguments which will be passed on to `pwbart`.

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

Value

Returns an object of type criskbart with predictions corresponding to newdata and newdata2.

Examples


data(transplant)

delta <- (as.numeric(transplant$event)-1)
## recode so that delta=1 is cause of interest; delta=2 otherwise
delta[delta==1] <- 4
delta[delta==2] <- 1
delta[delta>1] <- 2
table(delta, transplant$event)

times <- pmax(1, ceiling(transplant$futime/7)) ## weeks
##times <- pmax(1, ceiling(transplant$futime/30.5)) ## months
table(times)

typeO <- 1*(transplant$abo=='O')
typeA <- 1*(transplant$abo=='A')
typeB <- 1*(transplant$abo=='B')
typeAB <- 1*(transplant$abo=='AB')
table(typeA, typeO)

x.train <- cbind(typeO, typeA, typeB, typeAB)

x.test <- cbind(1, 0, 0, 0)
dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

## parallel::mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
        post <- mc.crisk.bart(x.train=x.train, times=times, delta=delta,
                               seed=99, mc.cores=2, nskip=5, ndpost=5,
                               keepevery=1)

        pre <- surv.pre.bart(x.train=x.train, x.test=x.test,
                             times=times, delta=delta)

        K <- post$K

        pred <- mc.crisk.pwbart(pre$tx.test, pre$tx.test,
                                post$treedraws, post$treedraws2,
                                post$binaryOffset, post$binaryOffset2)
}

## Not run: 

## run one long MCMC chain in one process
## set.seed(99)
## post <- crisk.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

## in the interest of time, consider speeding it up by parallel processing
## run "mc.cores" number of shorter MCMC chains in parallel processes
post <- mc.crisk.bart(x.train=x.train,
                       times=times, delta=delta,
                       x.test=x.test, seed=99, mc.cores=8)

## check <- mc.crisk.pwbart(post$tx.test, post$tx.test,
##                           post$treedraws, post$treedraws2,
##                           post$binaryOffset,
##                           post$binaryOffset2, mc.cores=8)
check <- predict(post, newdata=post$tx.test, newdata2=post$tx.test2,
                 mc.cores=8)

print(c(post$surv.test.mean[1], check$surv.test.mean[1],
        post$surv.test.mean[1]-check$surv.test.mean[1]), digits=22)

print(all(round(post$surv.test.mean, digits=9)==
    round(check$surv.test.mean, digits=9)))

print(c(post$cif.test.mean[1], check$cif.test.mean[1],
        post$cif.test.mean[1]-check$cif.test.mean[1]), digits=22)

print(all(round(post$cif.test.mean, digits=9)==
    round(check$cif.test.mean, digits=9)))

print(c(post$cif.test2.mean[1], check$cif.test2.mean[1],
        post$cif.test2.mean[1]-check$cif.test2.mean[1]), digits=22)

print(all(round(post$cif.test2.mean, digits=9)==
    round(check$cif.test2.mean, digits=9)))


## End(Not run)
data(transplant)

delta <- (as.numeric(transplant$event)-1)
## recode so that delta=1 is cause of interest; delta=2 otherwise
delta[delta==1] <- 4
delta[delta==2] <- 1
delta[delta>1] <- 2
table(delta, transplant$event)

times <- pmax(1, ceiling(transplant$futime/7)) ## weeks
##times <- pmax(1, ceiling(transplant$futime/30.5)) ## months
table(times)

typeO <- 1*(transplant$abo=='O')
typeA <- 1*(transplant$abo=='A')
typeB <- 1*(transplant$abo=='B')
typeAB <- 1*(transplant$abo=='AB')
table(typeA, typeO)

x.train <- cbind(typeO, typeA, typeB, typeAB)

x.test <- cbind(1, 0, 0, 0)
dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

## parallel::mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
        post <- mc.crisk.bart(x.train=x.train, times=times, delta=delta,
                               seed=99, mc.cores=2, nskip=5, ndpost=5,
                               keepevery=1)

        pre <- surv.pre.bart(x.train=x.train, x.test=x.test,
                             times=times, delta=delta)

        K <- post$K

        pred <- mc.crisk.pwbart(pre$tx.test, pre$tx.test,
                                post$treedraws, post$treedraws2,
                                post$binaryOffset, post$binaryOffset2)
}

## Not run: 

## run one long MCMC chain in one process
## set.seed(99)
## post <- crisk.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

## in the interest of time, consider speeding it up by parallel processing
## run "mc.cores" number of shorter MCMC chains in parallel processes
post <- mc.crisk.bart(x.train=x.train,
                       times=times, delta=delta,
                       x.test=x.test, seed=99, mc.cores=8)

## check <- mc.crisk.pwbart(post$tx.test, post$tx.test,
##                           post$treedraws, post$treedraws2,
##                           post$binaryOffset,
##                           post$binaryOffset2, mc.cores=8)
check <- predict(post, newdata=post$tx.test, newdata2=post$tx.test2,
                 mc.cores=8)

print(c(post$surv.test.mean[1], check$surv.test.mean[1],
        post$surv.test.mean[1]-check$surv.test.mean[1]), digits=22)

print(all(round(post$surv.test.mean, digits=9)==
    round(check$surv.test.mean, digits=9)))

print(c(post$cif.test.mean[1], check$cif.test.mean[1],
        post$cif.test.mean[1]-check$cif.test.mean[1]), digits=22)

print(all(round(post$cif.test.mean, digits=9)==
    round(check$cif.test.mean, digits=9)))

print(c(post$cif.test2.mean[1], check$cif.test2.mean[1],
        post$cif.test2.mean[1]-check$cif.test2.mean[1]), digits=22)

print(all(round(post$cif.test2.mean, digits=9)==
    round(check$cif.test2.mean, digits=9)))


## End(Not run)

Predicting new observations with a previously fitted BART model

Description

BART is a Bayesian “sum-of-trees” model.
For a numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .

$f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

## S3 method for class 'lbart'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)
## S3 method for class 'lbart'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)

Arguments

`object`	`object` returned from previous BART fit with `surv.bart` or `mc.surv.bart`.
`newdata`	Matrix of covariates to predict the distribution of $t$ .
`mc.cores`	Number of threads to utilize.
`openmp`	Logical value dictating whether OpenMP is utilized for parallel processing. Of course, this depends on whether OpenMP is available on your system which, by default, is verified with `mc.cores.openmp`.
`...`	Other arguments which will be passed on to `pwbart`.

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

Value

Returns an object of type lbart with predictions corresponding to newdata.

Examples


## load the advanced lung cancer example
data(lung)

group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno
times <- lung[group, 2]   ##lung$time
delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead
                          ##delta: 0=censored, 1=dead

## this study reports time in days rather than months like other studies
## coarsening from days to months will reduce the computational burden
times <- ceiling(times/30)

summary(times)
table(delta)

x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates

## lung$age:        Age in years
## lung$sex:        Male=1 Female=2
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)')

summary(x.train[ , 1])
table(x.train[ , 2])
table(x.train[ , 3])

x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios

dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

i <- 1

for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) {
    x.test[i, ] <- c(age, sex, ph.karno)
    i <- i+1
}

## this x.test is relatively small, but often you will want to
## predict for a large x.test matrix which may cause problems
## due to consumption of RAM so we can predict separately

## mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
    set.seed(99)
    post <- surv.bart(x.train=x.train, times=times, delta=delta, nskip=5, ndpost=5, keepevery=1)

    pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

    pred <- predict(post, pre$tx.test)
    ##pred. <- surv.pwbart(pre$tx.test, post$treedraws, post$binaryOffset)
}

## Not run: 
## run one long MCMC chain in one process
set.seed(99)
post <- surv.bart(x.train=x.train, times=times, delta=delta)

## run "mc.cores" number of shorter MCMC chains in parallel processes
## post <- mc.surv.bart(x.train=x.train, times=times, delta=delta,
##                      mc.cores=5, seed=99)

pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

pred <- predict(post, pre$tx.test)

## let's look at some survival curves
## first, a younger group with a healthier KPS
## age 50 with KPS=90: males and females
## males: row 17, females: row 23
x.test[c(17, 23), ]

low.risk.males <- 16*post$K+1:post$K ## K=unique times including censoring
low.risk.females <- 22*post$K+1:post$K

plot(post$times, pred$surv.test.mean[low.risk.males], type='s', col='blue',
     main='Age 50 with KPS=90', xlab='t', ylab='S(t)', ylim=c(0, 1))
points(post$times, pred$surv.test.mean[low.risk.females], type='s', col='red')


## End(Not run)
## load the advanced lung cancer example
data(lung)

group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno
times <- lung[group, 2]   ##lung$time
delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead
                          ##delta: 0=censored, 1=dead

## this study reports time in days rather than months like other studies
## coarsening from days to months will reduce the computational burden
times <- ceiling(times/30)

summary(times)
table(delta)

x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates

## lung$age:        Age in years
## lung$sex:        Male=1 Female=2
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)')

summary(x.train[ , 1])
table(x.train[ , 2])
table(x.train[ , 3])

x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios

dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

i <- 1

for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) {
    x.test[i, ] <- c(age, sex, ph.karno)
    i <- i+1
}

## this x.test is relatively small, but often you will want to
## predict for a large x.test matrix which may cause problems
## due to consumption of RAM so we can predict separately

## mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
    set.seed(99)
    post <- surv.bart(x.train=x.train, times=times, delta=delta, nskip=5, ndpost=5, keepevery=1)

    pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

    pred <- predict(post, pre$tx.test)
    ##pred. <- surv.pwbart(pre$tx.test, post$treedraws, post$binaryOffset)
}

## Not run: 
## run one long MCMC chain in one process
set.seed(99)
post <- surv.bart(x.train=x.train, times=times, delta=delta)

## run "mc.cores" number of shorter MCMC chains in parallel processes
## post <- mc.surv.bart(x.train=x.train, times=times, delta=delta,
##                      mc.cores=5, seed=99)

pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

pred <- predict(post, pre$tx.test)

## let's look at some survival curves
## first, a younger group with a healthier KPS
## age 50 with KPS=90: males and females
## males: row 17, females: row 23
x.test[c(17, 23), ]

low.risk.males <- 16*post$K+1:post$K ## K=unique times including censoring
low.risk.females <- 22*post$K+1:post$K

plot(post$times, pred$surv.test.mean[low.risk.males], type='s', col='blue',
     main='Age 50 with KPS=90', xlab='t', ylab='S(t)', ylim=c(0, 1))
points(post$times, pred$surv.test.mean[low.risk.females], type='s', col='red')


## End(Not run)

Predicting new observations with a previously fitted BART model

Description

BART is a Bayesian “sum-of-trees” model.
For a numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .

$f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

## S3 method for class 'mbart'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)
## S3 method for class 'mbart2'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)
## S3 method for class 'mbart'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)
## S3 method for class 'mbart2'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)

Arguments

`object`	`object` returned from previous BART fit with `mbart` or `mbart2`.
`newdata`	Matrix of covariates to predict the distribution of $t$ .
`mc.cores`	Number of threads to utilize.
`openmp`	Logical value dictating whether OpenMP is utilized for parallel processing. Of course, this depends on whether OpenMP is available on your system which, by default, is verified with `mc.cores.openmp`.
`...`	Other arguments which will be passed on to `pwbart`.

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

Value

Returns an object of type mbart with predictions corresponding to newdata.

Examples


## load the advanced lung cancer example
data(lung)

group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno
times <- lung[group, 2]   ##lung$time
delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead
                          ##delta: 0=censored, 1=dead

## this study reports time in days rather than months like other studies
## coarsening from days to months will reduce the computational burden
times <- ceiling(times/30)

summary(times)
table(delta)

x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates

## lung$age:        Age in years
## lung$sex:        Male=1 Female=2
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)')

summary(x.train[ , 1])
table(x.train[ , 2])
table(x.train[ , 3])

x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios

dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

i <- 1

for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) {
    x.test[i, ] <- c(age, sex, ph.karno)
    i <- i+1
}

## this x.test is relatively small, but often you will want to
## predict for a large x.test matrix which may cause problems
## due to consumption of RAM so we can predict separately

## mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
    set.seed(99)
    post <- surv.bart(x.train=x.train, times=times, delta=delta, nskip=5, ndpost=5, keepevery=1)

    pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

    pred <- predict(post, pre$tx.test)
    ##pred. <- surv.pwbart(pre$tx.test, post$treedraws, post$binaryOffset)
}

## Not run: 
## run one long MCMC chain in one process
set.seed(99)
post <- surv.bart(x.train=x.train, times=times, delta=delta)

## run "mc.cores" number of shorter MCMC chains in parallel processes
## post <- mc.surv.bart(x.train=x.train, times=times, delta=delta,
##                      mc.cores=5, seed=99)

pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

pred <- predict(post, pre$tx.test)

## let's look at some survival curves
## first, a younger group with a healthier KPS
## age 50 with KPS=90: males and females
## males: row 17, females: row 23
x.test[c(17, 23), ]

low.risk.males <- 16*post$K+1:post$K ## K=unique times including censoring
low.risk.females <- 22*post$K+1:post$K

plot(post$times, pred$surv.test.mean[low.risk.males], type='s', col='blue',
     main='Age 50 with KPS=90', xlab='t', ylab='S(t)', ylim=c(0, 1))
points(post$times, pred$surv.test.mean[low.risk.females], type='s', col='red')


## End(Not run)
## load the advanced lung cancer example
data(lung)

group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno
times <- lung[group, 2]   ##lung$time
delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead
                          ##delta: 0=censored, 1=dead

## this study reports time in days rather than months like other studies
## coarsening from days to months will reduce the computational burden
times <- ceiling(times/30)

summary(times)
table(delta)

x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates

## lung$age:        Age in years
## lung$sex:        Male=1 Female=2
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)')

summary(x.train[ , 1])
table(x.train[ , 2])
table(x.train[ , 3])

x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios

dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

i <- 1

for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) {
    x.test[i, ] <- c(age, sex, ph.karno)
    i <- i+1
}

## this x.test is relatively small, but often you will want to
## predict for a large x.test matrix which may cause problems
## due to consumption of RAM so we can predict separately

## mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
    set.seed(99)
    post <- surv.bart(x.train=x.train, times=times, delta=delta, nskip=5, ndpost=5, keepevery=1)

    pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

    pred <- predict(post, pre$tx.test)
    ##pred. <- surv.pwbart(pre$tx.test, post$treedraws, post$binaryOffset)
}

## Not run: 
## run one long MCMC chain in one process
set.seed(99)
post <- surv.bart(x.train=x.train, times=times, delta=delta)

## run "mc.cores" number of shorter MCMC chains in parallel processes
## post <- mc.surv.bart(x.train=x.train, times=times, delta=delta,
##                      mc.cores=5, seed=99)

pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

pred <- predict(post, pre$tx.test)

## let's look at some survival curves
## first, a younger group with a healthier KPS
## age 50 with KPS=90: males and females
## males: row 17, females: row 23
x.test[c(17, 23), ]

low.risk.males <- 16*post$K+1:post$K ## K=unique times including censoring
low.risk.females <- 22*post$K+1:post$K

plot(post$times, pred$surv.test.mean[low.risk.males], type='s', col='blue',
     main='Age 50 with KPS=90', xlab='t', ylab='S(t)', ylim=c(0, 1))
points(post$times, pred$surv.test.mean[low.risk.females], type='s', col='red')


## End(Not run)

Predicting new observations with a previously fitted BART model

Description

BART is a Bayesian “sum-of-trees” model.
For a numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .

$f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

## S3 method for class 'pbart'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)
## S3 method for class 'pbart'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)

Arguments

`object`	`object` returned from previous BART fit with `surv.bart` or `mc.surv.bart`.
`newdata`	Matrix of covariates to predict the distribution of $t$ .
`mc.cores`	Number of threads to utilize.
`openmp`	Logical value dictating whether OpenMP is utilized for parallel processing. Of course, this depends on whether OpenMP is available on your system which, by default, is verified with `mc.cores.openmp`.
`...`	Other arguments which will be passed on to `pwbart`.

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

Value

Returns an object of type pbart with predictions corresponding to newdata.

Examples


## load the advanced lung cancer example
data(lung)

group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno
times <- lung[group, 2]   ##lung$time
delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead
                          ##delta: 0=censored, 1=dead

## this study reports time in days rather than months like other studies
## coarsening from days to months will reduce the computational burden
times <- ceiling(times/30)

summary(times)
table(delta)

x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates

## lung$age:        Age in years
## lung$sex:        Male=1 Female=2
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)')

summary(x.train[ , 1])
table(x.train[ , 2])
table(x.train[ , 3])

x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios

dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

i <- 1

for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) {
    x.test[i, ] <- c(age, sex, ph.karno)
    i <- i+1
}

## this x.test is relatively small, but often you will want to
## predict for a large x.test matrix which may cause problems
## due to consumption of RAM so we can predict separately

## mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
    set.seed(99)
    post <- surv.bart(x.train=x.train, times=times, delta=delta, nskip=5, ndpost=5, keepevery=1)

    pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

    pred <- predict(post, pre$tx.test)
    ##pred. <- surv.pwbart(pre$tx.test, post$treedraws, post$binaryOffset)
}

## Not run: 
## run one long MCMC chain in one process
set.seed(99)
post <- surv.bart(x.train=x.train, times=times, delta=delta)

## run "mc.cores" number of shorter MCMC chains in parallel processes
## post <- mc.surv.bart(x.train=x.train, times=times, delta=delta,
##                      mc.cores=5, seed=99)

pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

pred <- predict(post, pre$tx.test)

## let's look at some survival curves
## first, a younger group with a healthier KPS
## age 50 with KPS=90: males and females
## males: row 17, females: row 23
x.test[c(17, 23), ]

low.risk.males <- 16*post$K+1:post$K ## K=unique times including censoring
low.risk.females <- 22*post$K+1:post$K

plot(post$times, pred$surv.test.mean[low.risk.males], type='s', col='blue',
     main='Age 50 with KPS=90', xlab='t', ylab='S(t)', ylim=c(0, 1))
points(post$times, pred$surv.test.mean[low.risk.females], type='s', col='red')


## End(Not run)
## load the advanced lung cancer example
data(lung)

group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno
times <- lung[group, 2]   ##lung$time
delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead
                          ##delta: 0=censored, 1=dead

## this study reports time in days rather than months like other studies
## coarsening from days to months will reduce the computational burden
times <- ceiling(times/30)

summary(times)
table(delta)

x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates

## lung$age:        Age in years
## lung$sex:        Male=1 Female=2
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)')

summary(x.train[ , 1])
table(x.train[ , 2])
table(x.train[ , 3])

x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios

dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

i <- 1

for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) {
    x.test[i, ] <- c(age, sex, ph.karno)
    i <- i+1
}

## this x.test is relatively small, but often you will want to
## predict for a large x.test matrix which may cause problems
## due to consumption of RAM so we can predict separately

## mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
    set.seed(99)
    post <- surv.bart(x.train=x.train, times=times, delta=delta, nskip=5, ndpost=5, keepevery=1)

    pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

    pred <- predict(post, pre$tx.test)
    ##pred. <- surv.pwbart(pre$tx.test, post$treedraws, post$binaryOffset)
}

## Not run: 
## run one long MCMC chain in one process
set.seed(99)
post <- surv.bart(x.train=x.train, times=times, delta=delta)

## run "mc.cores" number of shorter MCMC chains in parallel processes
## post <- mc.surv.bart(x.train=x.train, times=times, delta=delta,
##                      mc.cores=5, seed=99)

pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

pred <- predict(post, pre$tx.test)

## let's look at some survival curves
## first, a younger group with a healthier KPS
## age 50 with KPS=90: males and females
## males: row 17, females: row 23
x.test[c(17, 23), ]

low.risk.males <- 16*post$K+1:post$K ## K=unique times including censoring
low.risk.females <- 22*post$K+1:post$K

plot(post$times, pred$surv.test.mean[low.risk.males], type='s', col='blue',
     main='Age 50 with KPS=90', xlab='t', ylab='S(t)', ylim=c(0, 1))
points(post$times, pred$surv.test.mean[low.risk.females], type='s', col='red')


## End(Not run)

Predicting new observations with a previously fitted BART model

Description

BART is a Bayesian “sum-of-trees” model.
For a numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .

$f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

## S3 method for class 'recurbart'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)
## S3 method for class 'recurbart'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)

Arguments

`object`	`object` returned from previous BART fit with `recur.bart` or `mc.recur.bart`.
`newdata`	Matrix of covariates to predict the distribution of $t$ .
`mc.cores`	Number of threads to utilize.
`openmp`	Logical value dictating whether OpenMP is utilized for parallel processing. Of course, this depends on whether OpenMP is available on your system which, by default, is verified with `mc.cores.openmp`.
`...`	Other arguments which will be passed on to `pwbart`.

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

Value

Returns an object of type recurbart with predictions corresponding to newdata.

Examples

## load 20 percent random sample
data(xdm20.train)
data(xdm20.test)
data(ydm20.train)

##test BART with token run to ensure installation works
## with current technology even a token run will violate CRAN policy
## set.seed(99)
## post <- recur.bart(x.train=xdm20.train, y.train=ydm20.train,
##                    nskip=1, ndpost=1, keepevery=1)

## Not run: 
set.seed(99)
post <- recur.bart(x.train=xdm20.train, y.train=ydm20.train)
## larger data sets can take some time so, if parallel processing
## is available, submit this statement instead
## post <- mc.recur.bart(x.train=xdm20.train, y.train=ydm20.train,
##                      mc.cores=8, seed=99)

require(rpart)
require(rpart.plot)

dss <- rpart(post$yhat.train.mean~xdm20.train)

rpart.plot(dss)
## for the 20 percent sample, notice that the top splits
## involve cci_pvd and n
## for the full data set, notice that all splits
## involve ca, cci_pud, cci_pvd, ins270 and n
## (except one at the bottom involving a small group)

## compare patients treated with insulin (ins270=1) vs
## not treated with insulin (ins270=0)
N.train <- 50
N.test <- 50
K <- post$K ## 798 unique time points

## only testing set, i.e., remove training set
xdm20.test. <- xdm20.test[N.train*K+(1:(N.test*K)), ]
xdm20.test. <- rbind(xdm20.test., xdm20.test.)
xdm20.test.[ , 'ins270'] <- rep(0:1, each=N.test*K)

## multiple threads will be utilized if available
pred <- predict(post, xdm20.test., mc.cores=8)

## create Friedman's partial dependence function for the
## intensity/hazard by time and ins270
NK.test <- N.test*K
M <- nrow(pred$haz.test) ## number of MCMC samples, typically 1000

RI <- matrix(0, M, K)

for(i in 1:N.test)
    RI <- RI+(pred$haz.test[ , (N.test+i-1)*K+1:K]/
              pred$haz.test[ , (i-1)*K+1:K])/N.test

RI.lo <- apply(RI, 2, quantile, probs=0.025)
RI.mu <- apply(RI, 2, mean)
RI.hi <- apply(RI, 2, quantile, probs=0.975)

plot(post$times, RI.hi, type='l', lty=2, log='y',
     ylim=c(min(RI.lo, 1/RI.hi), max(1/RI.lo, RI.hi)),
     xlab='t', ylab='RI(t, x)',
     sub='insulin(ins270=1) vs. no insulin(ins270=0)',
     main='Relative intensity of hospital admissions for diabetics')
lines(post$times, RI.mu)
lines(post$times, RI.lo, lty=2)
lines(post$times, rep(1, K), col='darkgray')

## RI for insulin therapy seems fairly constant with time
mean(RI.mu)


## End(Not run)
## load 20 percent random sample
data(xdm20.train)
data(xdm20.test)
data(ydm20.train)

##test BART with token run to ensure installation works
## with current technology even a token run will violate CRAN policy
## set.seed(99)
## post <- recur.bart(x.train=xdm20.train, y.train=ydm20.train,
##                    nskip=1, ndpost=1, keepevery=1)

## Not run: 
set.seed(99)
post <- recur.bart(x.train=xdm20.train, y.train=ydm20.train)
## larger data sets can take some time so, if parallel processing
## is available, submit this statement instead
## post <- mc.recur.bart(x.train=xdm20.train, y.train=ydm20.train,
##                      mc.cores=8, seed=99)

require(rpart)
require(rpart.plot)

dss <- rpart(post$yhat.train.mean~xdm20.train)

rpart.plot(dss)
## for the 20 percent sample, notice that the top splits
## involve cci_pvd and n
## for the full data set, notice that all splits
## involve ca, cci_pud, cci_pvd, ins270 and n
## (except one at the bottom involving a small group)

## compare patients treated with insulin (ins270=1) vs
## not treated with insulin (ins270=0)
N.train <- 50
N.test <- 50
K <- post$K ## 798 unique time points

## only testing set, i.e., remove training set
xdm20.test. <- xdm20.test[N.train*K+(1:(N.test*K)), ]
xdm20.test. <- rbind(xdm20.test., xdm20.test.)
xdm20.test.[ , 'ins270'] <- rep(0:1, each=N.test*K)

## multiple threads will be utilized if available
pred <- predict(post, xdm20.test., mc.cores=8)

## create Friedman's partial dependence function for the
## intensity/hazard by time and ins270
NK.test <- N.test*K
M <- nrow(pred$haz.test) ## number of MCMC samples, typically 1000

RI <- matrix(0, M, K)

for(i in 1:N.test)
    RI <- RI+(pred$haz.test[ , (N.test+i-1)*K+1:K]/
              pred$haz.test[ , (i-1)*K+1:K])/N.test

RI.lo <- apply(RI, 2, quantile, probs=0.025)
RI.mu <- apply(RI, 2, mean)
RI.hi <- apply(RI, 2, quantile, probs=0.975)

plot(post$times, RI.hi, type='l', lty=2, log='y',
     ylim=c(min(RI.lo, 1/RI.hi), max(1/RI.lo, RI.hi)),
     xlab='t', ylab='RI(t, x)',
     sub='insulin(ins270=1) vs. no insulin(ins270=0)',
     main='Relative intensity of hospital admissions for diabetics')
lines(post$times, RI.mu)
lines(post$times, RI.lo, lty=2)
lines(post$times, rep(1, K), col='darkgray')

## RI for insulin therapy seems fairly constant with time
mean(RI.mu)


## End(Not run)

Predicting new observations with a previously fitted BART model

Description

BART is a Bayesian “sum-of-trees” model.
For a numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .

$f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

## S3 method for class 'survbart'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)
## S3 method for class 'survbart'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)

Arguments

`object`	`object` returned from previous BART fit with `surv.bart` or `mc.surv.bart`.
`newdata`	Matrix of covariates to predict the distribution of $t$ .
`mc.cores`	Number of threads to utilize.
`openmp`	Logical value dictating whether OpenMP is utilized for parallel processing. Of course, this depends on whether OpenMP is available on your system which, by default, is verified with `mc.cores.openmp`.
`...`	Other arguments which will be passed on to `pwbart`.

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

Value

Returns an object of type survbart with predictions corresponding to newdata.

Examples


## load the advanced lung cancer example
data(lung)

group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno
times <- lung[group, 2]   ##lung$time
delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead
                          ##delta: 0=censored, 1=dead

## this study reports time in days rather than months like other studies
## coarsening from days to months will reduce the computational burden
times <- ceiling(times/30)

summary(times)
table(delta)

x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates

## lung$age:        Age in years
## lung$sex:        Male=1 Female=2
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)')

summary(x.train[ , 1])
table(x.train[ , 2])
table(x.train[ , 3])

x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios

dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

i <- 1

for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) {
    x.test[i, ] <- c(age, sex, ph.karno)
    i <- i+1
}

## this x.test is relatively small, but often you will want to
## predict for a large x.test matrix which may cause problems
## due to consumption of RAM so we can predict separately

## mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
    set.seed(99)
    post <- surv.bart(x.train=x.train, times=times, delta=delta, nskip=5, ndpost=5, keepevery=1)

    pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

    pred <- predict(post, pre$tx.test)
    ##pred. <- surv.pwbart(pre$tx.test, post$treedraws, post$binaryOffset)
}

## Not run: 
## run one long MCMC chain in one process
set.seed(99)
post <- surv.bart(x.train=x.train, times=times, delta=delta)

## run "mc.cores" number of shorter MCMC chains in parallel processes
## post <- mc.surv.bart(x.train=x.train, times=times, delta=delta,
##                      mc.cores=5, seed=99)

pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

pred <- predict(post, pre$tx.test)

## let's look at some survival curves
## first, a younger group with a healthier KPS
## age 50 with KPS=90: males and females
## males: row 17, females: row 23
x.test[c(17, 23), ]

low.risk.males <- 16*post$K+1:post$K ## K=unique times including censoring
low.risk.females <- 22*post$K+1:post$K

plot(post$times, pred$surv.test.mean[low.risk.males], type='s', col='blue',
     main='Age 50 with KPS=90', xlab='t', ylab='S(t)', ylim=c(0, 1))
points(post$times, pred$surv.test.mean[low.risk.females], type='s', col='red')


## End(Not run)
## load the advanced lung cancer example
data(lung)

group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno
times <- lung[group, 2]   ##lung$time
delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead
                          ##delta: 0=censored, 1=dead

## this study reports time in days rather than months like other studies
## coarsening from days to months will reduce the computational burden
times <- ceiling(times/30)

summary(times)
table(delta)

x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates

## lung$age:        Age in years
## lung$sex:        Male=1 Female=2
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)')

summary(x.train[ , 1])
table(x.train[ , 2])
table(x.train[ , 3])

x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios

dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

i <- 1

for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) {
    x.test[i, ] <- c(age, sex, ph.karno)
    i <- i+1
}

## this x.test is relatively small, but often you will want to
## predict for a large x.test matrix which may cause problems
## due to consumption of RAM so we can predict separately

## mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
    set.seed(99)
    post <- surv.bart(x.train=x.train, times=times, delta=delta, nskip=5, ndpost=5, keepevery=1)

    pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

    pred <- predict(post, pre$tx.test)
    ##pred. <- surv.pwbart(pre$tx.test, post$treedraws, post$binaryOffset)
}

## Not run: 
## run one long MCMC chain in one process
set.seed(99)
post <- surv.bart(x.train=x.train, times=times, delta=delta)

## run "mc.cores" number of shorter MCMC chains in parallel processes
## post <- mc.surv.bart(x.train=x.train, times=times, delta=delta,
##                      mc.cores=5, seed=99)

pre <- surv.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

pred <- predict(post, pre$tx.test)

## let's look at some survival curves
## first, a younger group with a healthier KPS
## age 50 with KPS=90: males and females
## males: row 17, females: row 23
x.test[c(17, 23), ]

low.risk.males <- 16*post$K+1:post$K ## K=unique times including censoring
low.risk.females <- 22*post$K+1:post$K

plot(post$times, pred$surv.test.mean[low.risk.males], type='s', col='blue',
     main='Age 50 with KPS=90', xlab='t', ylab='S(t)', ylim=c(0, 1))
points(post$times, pred$surv.test.mean[low.risk.females], type='s', col='red')


## End(Not run)

Predicting new observations with a previously fitted BART model

Description

BART is a Bayesian “sum-of-trees” model.
For a numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .

$f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

## S3 method for class 'wbart'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)
## S3 method for class 'wbart'
predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)

Arguments

`object`	`object` returned from previous BART fit.
`newdata`	Matrix of covariates to predict $y$ for.
`mc.cores`	Number of threads to utilize.
`openmp`	Logical value dictating whether OpenMP is utilized for parallel processing. Of course, this depends on whether OpenMP is available on your system which, by default, is verified with `mc.cores.openmp`.
`...`	Other arguments which will be passed on to `pwbart`.

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

Value

Returns a matrix of predictions corresponding to newdata.

Examples

##simulate data (example from Friedman MARS paper)
f = function(x){
10*sin(pi*x[,1]*x[,2]) + 20*(x[,3]-.5)^2+10*x[,4]+5*x[,5]
}
sigma = 1.0  #y = f(x) + sigma*z , z~N(0,1)
n = 100      #number of observations
set.seed(99)
x=matrix(runif(n*10),n,10) #10 variables, only first 5 matter
y=f(x)

##test BART with token run to ensure installation works
set.seed(99)
post = wbart(x,y,nskip=5,ndpost=5)
x.test = matrix(runif(500*10),500,10)

## Not run: 
##run BART
set.seed(99)
post = wbart(x,y)
x.test = matrix(runif(500*10),500,10)
pred = predict(post, x.test, mu=mean(y))

plot(apply(pred, 2, mean), f(x.test))


## End(Not run)
##simulate data (example from Friedman MARS paper)
f = function(x){
10*sin(pi*x[,1]*x[,2]) + 20*(x[,3]-.5)^2+10*x[,4]+5*x[,5]
}
sigma = 1.0  #y = f(x) + sigma*z , z~N(0,1)
n = 100      #number of observations
set.seed(99)
x=matrix(runif(n*10),n,10) #10 variables, only first 5 matter
y=f(x)

##test BART with token run to ensure installation works
set.seed(99)
post = wbart(x,y,nskip=5,ndpost=5)
x.test = matrix(runif(500*10),500,10)

## Not run: 
##run BART
set.seed(99)
post = wbart(x,y)
x.test = matrix(runif(500*10),500,10)
pred = predict(post, x.test, mu=mean(y))

plot(apply(pred, 2, mean), f(x.test))


## End(Not run)

Predicting new observations with a previously fitted BART model

Description

BART is a Bayesian “sum-of-trees” model.
For a numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .

$f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

pwbart( x.test, treedraws, mu=0, mc.cores=1L, transposed=FALSE,
        dodraws=TRUE,
        nice=19L ## mc.pwbart only
      )

mc.pwbart( x.test, treedraws, mu=0, mc.cores=2L, transposed=FALSE,
           dodraws=TRUE,
           nice=19L ## mc.pwbart only
         )
pwbart( x.test, treedraws, mu=0, mc.cores=1L, transposed=FALSE,
        dodraws=TRUE,
        nice=19L ## mc.pwbart only
      )

mc.pwbart( x.test, treedraws, mu=0, mc.cores=2L, transposed=FALSE,
           dodraws=TRUE,
           nice=19L ## mc.pwbart only
         )

Arguments

`x.test`	Matrix of covariates to predict $y$ for.
`treedraws`	`$treedraws` returned from `wbart` or `pbart`.
`mu`	Mean to add on to $y$ prediction.
`mc.cores`	Number of threads to utilize.
`transposed`	When running `pwbart` or `mc.pwbart` in parallel, it is more memory-efficient to transpose `x.test` prior to calling the internal versions of these functions.
`dodraws`	Whether to return the draws themselves (the default), or whether to return the mean of the draws as specified by `dodraws=FALSE`.
`nice`	Set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest).

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

Value

Returns a matrix of predictions corresponding to x.test.

Examples

##simulate data (example from Friedman MARS paper)
f = function(x){
10*sin(pi*x[,1]*x[,2]) + 20*(x[,3]-.5)^2+10*x[,4]+5*x[,5]
}
sigma = 1.0  #y = f(x) + sigma*z , z~N(0,1)
n = 100      #number of observations
set.seed(99)
x=matrix(runif(n*10),n,10) #10 variables, only first 5 matter
y=f(x)

##test BART with token run to ensure installation works
set.seed(99)
post = wbart(x,y,nskip=5,ndpost=5)
x.test = matrix(runif(500*10),500,10)

## Not run: 
##run BART
set.seed(99)
post = wbart(x,y)
x.test = matrix(runif(500*10),500,10)
pred = pwbart(post$treedraws, x.test, mu=mean(y))

plot(apply(pred, 2, mean), f(x.test))


## End(Not run)
##simulate data (example from Friedman MARS paper)
f = function(x){
10*sin(pi*x[,1]*x[,2]) + 20*(x[,3]-.5)^2+10*x[,4]+5*x[,5]
}
sigma = 1.0  #y = f(x) + sigma*z , z~N(0,1)
n = 100      #number of observations
set.seed(99)
x=matrix(runif(n*10),n,10) #10 variables, only first 5 matter
y=f(x)

##test BART with token run to ensure installation works
set.seed(99)
post = wbart(x,y,nskip=5,ndpost=5)
x.test = matrix(runif(500*10),500,10)

## Not run: 
##run BART
set.seed(99)
post = wbart(x,y)
x.test = matrix(runif(500*10),500,10)
pred = pwbart(post$treedraws, x.test, mu=mean(y))

plot(apply(pred, 2, mean), f(x.test))


## End(Not run)

BART for recurrent events

Description

Here we have implemented a simple and direct approach to utilize BART in survival analysis that is very flexible, and is akin to discrete-time survival analysis. Following the capabilities of BART, we allow for maximum flexibility in modeling the dependence of survival times on covariates. In particular, we do not impose proportional hazards.

To elaborate, consider data in the usual form: $(t_i, \delta_i, {x}_i)$ where $t_i$ is the event time, $\delta_i$ is an indicator distinguishing events ( $\delta=1$ ) from right-censoring ( $\delta=0$ ), ${x}_i$ is a vector of covariates, and $i=1, ..., N$ indexes subjects.

We denote the $K$ distinct event/censoring times by $0<t_{(1)}<...<t_{(K)}<\infty$ thus taking $t_{(j)}$ to be the $j^{th}$ order statistic among distinct observation times and, for convenience, $t_{(0)}=0$ . Now consider event indicators $y_{ij}$ for each subject $i$ at each distinct time $t_{(j)}$ up to and including the subject's observation time $t_i=t_{(n_i)}$ with $n_i=\sum_j I[t_{(j)}\leq t_i]$ . This means $y_{ij}=0$ if $j<n_i$ and $y_{in_i}=\delta_i$ .

We then denote by $p_{ij}$ the probability of an event at time $t_{(j)}$ conditional on no previous event. We now write the model for $y_{ij}$ as a nonparametric probit regression of $y_{ij}$ on the time $t_{(j)}$ and the covariates ${x}_i$ , and then utilize BART for binary responses. Specifically, $y_{ij}\ =\ \delta_i I[t_i=t_{(j)}],\ j=1, ..., n_i$ ; we have $p_{ij} = F(\mu_{ij}),\ \mu_{ij} = \mu_0+f(t_{(j)}, {x}_i)$ where $F$ denotes the standard normal cdf (probit link). As in the binary response case, $f$ is the sum of many tree models.

Usage


recur.bart(x.train=matrix(0,0,0),
           y.train=NULL, times=NULL, delta=NULL,
           x.test=matrix(0,0,0), x.test.nogrid=FALSE,
           sparse=FALSE, theta=0, omega=1,
           a=0.5, b=1, augment=FALSE, rho=NULL,
           xinfo=matrix(0,0,0), usequants=FALSE,
           
           rm.const=TRUE, type='pbart',
           ntype=as.integer(
               factor(type, levels=c('wbart', 'pbart', 'lbart'))),
           k=2, power=2, base=0.95,
           offset=NULL, tau.num=c(NA, 3, 6)[ntype], 
           ntree=50, numcut = 100L, ndpost=1000, nskip=250,
           keepevery=10, 
           
           
           printevery = 100L, 
           keeptrainfits = TRUE,
           seed=99,    ## mc.recur.bart only
           mc.cores=2, ## mc.recur.bart only
           nice=19L    ## mc.recur.bart only
         )

mc.recur.bart(x.train=matrix(0,0,0),
              y.train=NULL, times=NULL, delta=NULL,
              x.test=matrix(0,0,0), x.test.nogrid=FALSE,
              sparse=FALSE, theta=0, omega=1,
              a=0.5, b=1, augment=FALSE, rho=NULL,
              xinfo=matrix(0,0,0), usequants=FALSE,
              
              rm.const=TRUE, type='pbart',
              ntype=as.integer(
                  factor(type, levels=c('wbart', 'pbart', 'lbart'))),
              k=2, power=2, base=0.95,
              offset=NULL, tau.num=c(NA, 3, 6)[ntype], 
              ntree=50, numcut = 100L, ndpost=1000, nskip=250,
              keepevery=10, 
              
              
              printevery = 100L, 
              keeptrainfits = TRUE,
              seed=99,    ## mc.recur.bart only
              mc.cores=2, ## mc.recur.bart only
              nice=19L    ## mc.recur.bart only
            )
recur.bart(x.train=matrix(0,0,0),
           y.train=NULL, times=NULL, delta=NULL,
           x.test=matrix(0,0,0), x.test.nogrid=FALSE,
           sparse=FALSE, theta=0, omega=1,
           a=0.5, b=1, augment=FALSE, rho=NULL,
           xinfo=matrix(0,0,0), usequants=FALSE,
           
           rm.const=TRUE, type='pbart',
           ntype=as.integer(
               factor(type, levels=c('wbart', 'pbart', 'lbart'))),
           k=2, power=2, base=0.95,
           offset=NULL, tau.num=c(NA, 3, 6)[ntype], 
           ntree=50, numcut = 100L, ndpost=1000, nskip=250,
           keepevery=10, 
           
           
           printevery = 100L, 
           keeptrainfits = TRUE,
           seed=99,    ## mc.recur.bart only
           mc.cores=2, ## mc.recur.bart only
           nice=19L    ## mc.recur.bart only
         )

mc.recur.bart(x.train=matrix(0,0,0),
              y.train=NULL, times=NULL, delta=NULL,
              x.test=matrix(0,0,0), x.test.nogrid=FALSE,
              sparse=FALSE, theta=0, omega=1,
              a=0.5, b=1, augment=FALSE, rho=NULL,
              xinfo=matrix(0,0,0), usequants=FALSE,
              
              rm.const=TRUE, type='pbart',
              ntype=as.integer(
                  factor(type, levels=c('wbart', 'pbart', 'lbart'))),
              k=2, power=2, base=0.95,
              offset=NULL, tau.num=c(NA, 3, 6)[ntype], 
              ntree=50, numcut = 100L, ndpost=1000, nskip=250,
              keepevery=10, 
              
              
              printevery = 100L, 
              keeptrainfits = TRUE,
              seed=99,    ## mc.recur.bart only
              mc.cores=2, ## mc.recur.bart only
              nice=19L    ## mc.recur.bart only
            )

Arguments

`x.train`	Explanatory variables for training (in sample) data. Must be a matrix with (as usual) rows corresponding to observations and columns to variables. `recur.bart` will generate draws of $f(t, x)$ for each $x$ which is a row of x.train (note that the definition of `x.train` is dependent on whether `y.train` has been specified; see below).
`y.train`	Binary response dependent variable for training (in sample) data. If `y.train` is `NULL`, then `y.train` (`x.train` and `x.test`, if specified) are generated by a call to `recur.pre.bart` (which require that `times` and `delta` be provided: see below); otherwise, `y.train` (`x.train` and `x.test`, if specified) are utilized as given assuming that the data construction has already been performed.
`times`	The time of event or right-censoring. If `y.train` is `NULL`, then `times` (and `delta`) must be provided.
`delta`	The event indicator: 1 is an event while 0 is censored. If `y.train` is `NULL`, then `delta` (and `times`) must be provided.
`x.test`	Explanatory variables for test (out of sample) data. Must be a matrix and have the same structure as x.train. `recur.bart` will generate draws of $f(t, x)$ for each $x$ which is a row of x.test.
`x.test.nogrid`	Occasionally, you do not need the entire time grid for `x.test`. If so, then for performance reasons, you can set this argument to `TRUE`.
`sparse`	Whether to perform variable selection based on a sparse Dirichlet prior rather than simply uniform; see Linero 2016.
`theta`	Set $theta$ parameter; zero means random.
`omega`	Set $omega$ parameter; zero means random.
`a`	Sparse parameter for $Beta(a, b)$ prior: $0.5<=a<=1$ where lower values inducing more sparsity.
`b`	Sparse parameter for $Beta(a, b)$ prior; typically, $b=1$ .
`rho`	Sparse parameter: typically $rho=p$ where $p$ is the number of covariates under consideration.
`augment`	Whether data augmentation is to be performed in sparse variable selection.
`xinfo`	You can provide the cutpoints to BART or let BART choose them for you. To provide them, use the `xinfo` argument to specify a list (matrix) where the items (rows) are the covariates and the contents of the items (columns) are the cutpoints.
`usequants`	If `usequants=FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, uniform quantiles are used for the cutpoints.
`rm.const`	Whether or not to remove constant variables.
`type`	Whether to employ Albert-Chib, `'pbart'`, or Holmes-Held, `'lbart'`.
`ntype`	The integer equivalent of `type` where `'wbart'` is 1, `'pbart'` is 2 and `'lbart'` is 3.
`k`	k is the number of prior standard deviations $f(t, x)$ is away from +/-3. The bigger k is, the more conservative the fitting will be.
`power`	Power parameter for tree prior.
`base`	Base parameter for tree prior.
`offset`	With binary BART, the centering is $P(Y=1 \| x) = F(f(x) + offset)$ where `offset` defaults to `F^{-1}(mean(y.train))`. You can use the `offset` parameter to over-ride these defaults.
`tau.num`	The numerator in the `tau` definition, i.e., `tau=tau.num/(k*sqrt(ntree))`.
`ntree`	The number of trees in the sum.
`numcut`	The number of possible values of c (see usequants). If a single number if given, this is used for all variables. Otherwise a vector with length equal to ncol(x.train) is required, where the $i^{th}$ element gives the number of c used for the $i^{th}$ variable in x.train. If usequants is false, numcut equally spaced cutoffs are used covering the range of values in the corresponding column of x.train. If usequants is true, then min(numcut, the number of unique values in the corresponding columns of x.train - 1) c values are used.
`ndpost`	The number of posterior draws returned.
`nskip`	Number of MCMC iterations to be treated as burn in.
`keepevery`	Every keepevery draw is kept to be returned to the user.
`printevery`	As the MCMC runs, a message is printed every printevery draws.
`keeptrainfits`	Whether to keep `yhat.train` or not.
`seed`	`mc.recur.bart` only: seed required for reproducible MCMC.
`mc.cores`	`mc.recur.bart` only: number of cores to employ in parallel.
`nice`	`mc.recur.bart` only: set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest).

Value

recur.bart returns an object of type recurbart which is essentially a list. Besides the items listed below, the list has a binaryOffset component giving the value used, a times component giving the unique times, K which is the number of unique times, tx.train and tx.test, if any.

`yhat.train`	A matrix with ndpost rows and nrow(x.train) columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to a row of x.train. The $(i,j)$ value is $f^(t, x)$ for the $i^{th}$ kept draw of $f$ and the $j^{th}$ row of x.train. Burn-in is dropped.
`haz.train`	The hazard function, $h(t\|x)$ , where x's are the rows of the training data.
`cum.train`	The cumulative hazard function, $h(t\|x)$ , where x's are the rows of the training data.
`yhat.test`	Same as yhat.train but now the x's are the rows of the test data.
`haz.test`	The hazard function, $h(t\|x)$ , where x's are the rows of the test data.
`cum.test`	The cumulative hazard function, $h(t\|x)$ , where x's are the rows of the test data.
`varcount`	a matrix with ndpost rows and nrow(x.train) columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times that variable is used in a tree decision rule (over all trees) is given.

Note that yhat.train and yhat.test are $f(t, x)$ + binaryOffset. If you want draws of the probability $P(Y=1 | t, x)$ you need to apply the normal cdf (pnorm) to these values.

Examples


## load 20 percent random sample
data(xdm20.train)
data(xdm20.test)
data(ydm20.train)

##test BART with token run to ensure installation works
## with current technology even a token run will violate CRAN policy
## set.seed(99)
## post <- recur.bart(x.train=xdm20.train, y.train=ydm20.train,
##                    nskip=1, ndpost=1, keepevery=1)

## Not run: 

## set.seed(99)
## post <- recur.bart(x.train=xdm20.train, y.train=ydm20.train,
##                    keeptrainfits=TRUE)

## larger data sets can take some time so, if parallel processing
## is available, submit this statement instead
post <- mc.recur.bart(x.train=xdm20.train, y.train=ydm20.train,
                      keeptrainfits=TRUE, mc.cores=8, seed=99)

require(rpart)
require(rpart.plot)

post$yhat.train.mean <- apply(post$yhat.train, 2, mean)
dss <- rpart(post$yhat.train.mean~xdm20.train)

rpart.plot(dss)
## for the 20 percent sample, notice that the top splits
## involve cci_pvd and n
## for the full data set, notice that all splits
## involve ca, cci_pud, cci_pvd, ins270 and n
## (except one at the bottom involving a small group)

## compare patients treated with insulin (ins270=1) vs
## not treated with insulin (ins270=0)
N <- 50 ## 50 training patients and 50 validation patients
K <- post$K ## 798 unique time points
NK <- 50*K

## only testing set, i.e., remove training set
xdm20.test. <- xdm20.test[NK+1:NK, post$rm.const]
xdm20.test. <- rbind(xdm20.test., xdm20.test.)
xdm20.test.[ , 'ins270'] <- rep(0:1, each=NK)

## multiple threads will be utilized if available
pred <- predict(post, xdm20.test., mc.cores=8)

## create Friedman's partial dependence function for the
## relative intensity for ins270 by time
M <- nrow(pred$haz.test) ## number of MCMC samples
RI <- matrix(0, M, K)
for(j in 1:K) {
    h <- seq(j, NK, by=K)
    RI[ , j] <- apply(pred$haz.test[ , h+NK]/
                      pred$haz.test[ , h], 1, mean)
}

RI.lo <- apply(RI, 2, quantile, probs=0.025)
RI.mu <- apply(RI, 2, mean)
RI.hi <- apply(RI, 2, quantile, probs=0.975)

plot(post$times, RI.hi, type='l', lty=2, log='y',
     ylim=c(min(RI.lo, 1/RI.hi), max(1/RI.lo, RI.hi)),
     xlab='t', ylab='RI(t, x)',
     sub='insulin(ins270=1) vs. no insulin(ins270=0)',
     main='Relative intensity of hospital admissions for diabetics')
lines(post$times, RI.mu)
lines(post$times, RI.lo, lty=2)
lines(post$times, rep(1, K), col='darkgray')

## RI for insulin therapy seems fairly constant with time
mean(RI.mu)


## End(Not run)
## load 20 percent random sample
data(xdm20.train)
data(xdm20.test)
data(ydm20.train)

##test BART with token run to ensure installation works
## with current technology even a token run will violate CRAN policy
## set.seed(99)
## post <- recur.bart(x.train=xdm20.train, y.train=ydm20.train,
##                    nskip=1, ndpost=1, keepevery=1)

## Not run: 

## set.seed(99)
## post <- recur.bart(x.train=xdm20.train, y.train=ydm20.train,
##                    keeptrainfits=TRUE)

## larger data sets can take some time so, if parallel processing
## is available, submit this statement instead
post <- mc.recur.bart(x.train=xdm20.train, y.train=ydm20.train,
                      keeptrainfits=TRUE, mc.cores=8, seed=99)

require(rpart)
require(rpart.plot)

post$yhat.train.mean <- apply(post$yhat.train, 2, mean)
dss <- rpart(post$yhat.train.mean~xdm20.train)

rpart.plot(dss)
## for the 20 percent sample, notice that the top splits
## involve cci_pvd and n
## for the full data set, notice that all splits
## involve ca, cci_pud, cci_pvd, ins270 and n
## (except one at the bottom involving a small group)

## compare patients treated with insulin (ins270=1) vs
## not treated with insulin (ins270=0)
N <- 50 ## 50 training patients and 50 validation patients
K <- post$K ## 798 unique time points
NK <- 50*K

## only testing set, i.e., remove training set
xdm20.test. <- xdm20.test[NK+1:NK, post$rm.const]
xdm20.test. <- rbind(xdm20.test., xdm20.test.)
xdm20.test.[ , 'ins270'] <- rep(0:1, each=NK)

## multiple threads will be utilized if available
pred <- predict(post, xdm20.test., mc.cores=8)

## create Friedman's partial dependence function for the
## relative intensity for ins270 by time
M <- nrow(pred$haz.test) ## number of MCMC samples
RI <- matrix(0, M, K)
for(j in 1:K) {
    h <- seq(j, NK, by=K)
    RI[ , j] <- apply(pred$haz.test[ , h+NK]/
                      pred$haz.test[ , h], 1, mean)
}

RI.lo <- apply(RI, 2, quantile, probs=0.025)
RI.mu <- apply(RI, 2, mean)
RI.hi <- apply(RI, 2, quantile, probs=0.975)

plot(post$times, RI.hi, type='l', lty=2, log='y',
     ylim=c(min(RI.lo, 1/RI.hi), max(1/RI.lo, RI.hi)),
     xlab='t', ylab='RI(t, x)',
     sub='insulin(ins270=1) vs. no insulin(ins270=0)',
     main='Relative intensity of hospital admissions for diabetics')
lines(post$times, RI.mu)
lines(post$times, RI.lo, lty=2)
lines(post$times, rep(1, K), col='darkgray')

## RI for insulin therapy seems fairly constant with time
mean(RI.mu)


## End(Not run)

Data construction for recurrent events with BART

Description

Recurrent event data contained in $(t_1,\delta_1, ..., t_k,\delta_k, x)$ must be translated to data suitable for the BART model; see recur.bart for more details.

Usage

recur.pre.bart( times, delta, x.train=NULL, tstop=NULL, last.value=TRUE )
recur.pre.bart( times, delta, x.train=NULL, tstop=NULL, last.value=TRUE )

Arguments

`times`	Matrix of time to event or right-censoring.
`delta`	Matrix of event indicators: 1 is an event while 0 is censored.
`x.train`	Explanatory variables for training (in sample) data. If provided, must be a matrix with (as usual) rows corresponding to observations and columns to variables.
`tstop`	For non-instantaneous events, this the matrix of event stop times, i.e., between `times[i, j]` and `tstop[i, j]` subject `i` is not in the risk set for a recurrent event. N.B. This is NOT for counting process notation.
`last.value`	If `last.value=TRUE`, then the sojourn time, `v`, and the number of previous events, `N`, are carried forward assuming that no new events occur beyond censoring. If `last.value=FALSE`, then these variables are coded `NA` for easy identification allowing replacement with the desired values.

Value

recur.pre.bart returns a list. Besides the items listed below, the list has a times component giving the unique times and K which is the number of unique times.

`y.train`	A vector of binary responses.
`tx.train`	A matrix with the rows of the training data.
`tx.test`	Generated from `x.train` (see discussion above included in the argument `last.value`).

Examples


data(bladder)
subset <- -which(bladder1$stop==0)
bladder0 <- bladder1[subset, ]
id <- unique(sort(bladder0$id))
N <- length(id)
L <- max(bladder0$enum)

times <- matrix(0, nrow=N, ncol=L)
dimnames(times)[[1]] <- paste0(id)

delta <- matrix(0, nrow=N, ncol=L)
dimnames(delta)[[1]] <- paste0(id)

x.train <- matrix(NA, nrow=N, ncol=3+2*L) ## add time-dependent cols too
dimnames(x.train)[[1]] <- paste0(id)
dimnames(x.train)[[2]] <- c('Pl', 'B6', 'Th', rep(c('number', 'size'), L))

for(i in 1:N) {
    h <- id[i]

    for(j in 1:L) {
        k <- which(bladder0$id==h & bladder0$enum==j)

        if(length(k)==1) {
            times[i, j] <- bladder0$stop[k]
            delta[i, j] <- (bladder0$status[k]==1)*1

            if(j==1) {
                x.train[i, 1] <- as.numeric(bladder0$treatment[k])==1
                x.train[i, 2] <- as.numeric(bladder0$treatment[k])==2
                x.train[i, 3] <- as.numeric(bladder0$treatment[k])==3
                x.train[i, 4] <- bladder0$number[k]
                x.train[i, 5] <- bladder0$size[k]
            }
            else if(delta[i, j]==1) {
                if(bladder0$rtumor[k]!='.')
                    x.train[i, 2*j+2] <- as.numeric(bladder0$rtumor[k])
                if(bladder0$rsize[k]!='.')
                    x.train[i, 2*j+3] <- as.numeric(bladder0$rsize[k])
            }
        }
    }
}

pre <- recur.pre.bart(times=times, delta=delta, x.train=x.train)

J <- nrow(pre$tx.train)
for(j in 1:J) {
    if(pre$tx.train[j, 3]>0) {
        pre$tx.train[j, 7] <- pre$tx.train[j, 7+pre$tx.train[j, 3]*2]
        pre$tx.train[j, 8] <- pre$tx.train[j, 8+pre$tx.train[j, 3]*2]
    }
}
pre$tx.train <- pre$tx.train[ , 1:8]

K <- pre$K
NK <- N*K
for(j in 1:NK) {
    if(pre$tx.test[j, 3]>0) {
        pre$tx.test[j, 7] <- pre$tx.test[j, 7+pre$tx.test[j, 3]*2]
        pre$tx.test[j, 8] <- pre$tx.test[j, 8+pre$tx.test[j, 3]*2]
    }
}
pre$tx.test <- pre$tx.test[ , 1:8]


## in bladder1 both number and size are recorded as integers
## from 1 to 8 however they are often missing for recurrences
## at baseline there are no missing and 1 is the mode of both
pre$tx.train[which(is.na(pre$tx.train[ , 7])), 7] <- 1
pre$tx.train[which(is.na(pre$tx.train[ , 8])), 8] <- 1
pre$tx.test[which(is.na(pre$tx.test[ , 7])), 7] <- 1
pre$tx.test[which(is.na(pre$tx.test[ , 8])), 8] <- 1

## it is a good idea to explore more sophisticated methods
## such as imputing the missing data with Sequential BART
## Xu, Daniels and Winterstein.  Sequential BART for imputation of missing
## covariates.  Biostatistics 2016 doi: 10.1093/biostatistics/kxw009
## http://biostatistics.oxfordjournals.org/content/early/2016/03/15/biostatistics.kxw009/suppl/DC1
## https://cran.r-project.org/package=sbart
## library(sbart)
## set.seed(21)
## train <- seqBART(xx=pre$tx.train, yy=NULL, datatype=rep(0, 6),
##                type=0, numskip=20, burn=1000)
## coarsen the imputed data same way as observed example data
## train$imputed5[which(train$imputed5[ , 7]<1), 7] <- 1
## train$imputed5[which(train$imputed5[ , 7]>8), 7] <- 8
## train$imputed5[ , 7] <- round(train$imputed5[ , 7])
## train$imputed5[which(train$imputed5[ , 8]<1), 8] <- 1
## train$imputed5[which(train$imputed5[ , 8]>8), 8] <- 8
## train$imputed5[ , 8] <- round(train$imputed5[ , 8])

## for Friedman's partial dependence, we need to estimate the whole cohort
## at each treatment assignment (and, average over those)
pre$tx.test <- rbind(pre$tx.test, pre$tx.test, pre$tx.test)
pre$tx.test[ , 4] <- c(rep(1, NK), rep(0, 2*NK))          ## Pl
pre$tx.test[ , 5] <- c(rep(0, NK), rep(1, NK), rep(0, NK))## B6
pre$tx.test[ , 6] <- c(rep(0, 2*NK), rep(1, NK))          ## Th

## Not run: 
## set.seed(99)
## post <- recur.bart(y.train=pre$y.train, x.train=pre$tx.train, x.test=pre$tx.test)
## depending on your performance, you may want to run in parallel if available
post <- mc.recur.bart(y.train=pre$y.train, x.train=pre$tx.train,
                      x.test=pre$tx.test, mc.cores=8, seed=99)

M <- nrow(post$yhat.test)
RI.B6.Pl <- matrix(0, nrow=M, ncol=K)
RI.Th.Pl <- matrix(0, nrow=M, ncol=K)
RI.Th.B6 <- matrix(0, nrow=M, ncol=K)

for(j in 1:K) {
    h <- seq(j, NK, K)
    RI.B6.Pl[ , j] <- apply(post$prob.test[ , h+NK]/
                            post$prob.test[ , h], 1, mean)
    RI.Th.Pl[ , j] <- apply(post$prob.test[ , h+2*NK]/
                            post$prob.test[ , h], 1, mean)
    RI.Th.B6[ , j] <- apply(post$prob.test[ , h+2*NK]/
                            post$prob.test[ , h+NK], 1, mean)
}

RI.B6.Pl.mu <- apply(RI.B6.Pl, 2, mean)
RI.B6.Pl.025 <- apply(RI.B6.Pl, 2, quantile, probs=0.025)
RI.B6.Pl.975 <- apply(RI.B6.Pl, 2, quantile, probs=0.975)

RI.Th.Pl.mu <- apply(RI.Th.Pl, 2, mean)
RI.Th.Pl.025 <- apply(RI.Th.Pl, 2, quantile, probs=0.025)
RI.Th.Pl.975 <- apply(RI.Th.Pl, 2, quantile, probs=0.975)

RI.Th.B6.mu <- apply(RI.Th.B6, 2, mean)
RI.Th.B6.025 <- apply(RI.Th.B6, 2, quantile, probs=0.025)
RI.Th.B6.975 <- apply(RI.Th.B6, 2, quantile, probs=0.975)

plot(post$times, RI.Th.Pl.mu, col='blue',
     log='y', main='Bladder cancer ex: Thiotepa vs. Placebo',
     type='l', ylim=c(0.1, 10), ylab='RI(t)', xlab='t (months)')
lines(post$times, RI.Th.Pl.025, col='red')
lines(post$times, RI.Th.Pl.975, col='red')
abline(h=1)

plot(post$times, RI.B6.Pl.mu, col='blue',
     log='y', main='Bladder cancer ex: Vitamin B6 vs. Placebo',
     type='l', ylim=c(0.1, 10), ylab='RI(t)', xlab='t (months)')
lines(post$times, RI.B6.Pl.025, col='red')
lines(post$times, RI.B6.Pl.975, col='red')
abline(h=1)

plot(post$times, RI.Th.B6.mu, col='blue',
     log='y', main='Bladder cancer ex: Thiotepa vs. Vitamin B6',
     type='l', ylim=c(0.1, 10), ylab='RI(t)', xlab='t (months)')
lines(post$times, RI.Th.B6.025, col='red')
lines(post$times, RI.Th.B6.975, col='red')
abline(h=1)


## End(Not run)
data(bladder)
subset <- -which(bladder1$stop==0)
bladder0 <- bladder1[subset, ]
id <- unique(sort(bladder0$id))
N <- length(id)
L <- max(bladder0$enum)

times <- matrix(0, nrow=N, ncol=L)
dimnames(times)[[1]] <- paste0(id)

delta <- matrix(0, nrow=N, ncol=L)
dimnames(delta)[[1]] <- paste0(id)

x.train <- matrix(NA, nrow=N, ncol=3+2*L) ## add time-dependent cols too
dimnames(x.train)[[1]] <- paste0(id)
dimnames(x.train)[[2]] <- c('Pl', 'B6', 'Th', rep(c('number', 'size'), L))

for(i in 1:N) {
    h <- id[i]

    for(j in 1:L) {
        k <- which(bladder0$id==h & bladder0$enum==j)

        if(length(k)==1) {
            times[i, j] <- bladder0$stop[k]
            delta[i, j] <- (bladder0$status[k]==1)*1

            if(j==1) {
                x.train[i, 1] <- as.numeric(bladder0$treatment[k])==1
                x.train[i, 2] <- as.numeric(bladder0$treatment[k])==2
                x.train[i, 3] <- as.numeric(bladder0$treatment[k])==3
                x.train[i, 4] <- bladder0$number[k]
                x.train[i, 5] <- bladder0$size[k]
            }
            else if(delta[i, j]==1) {
                if(bladder0$rtumor[k]!='.')
                    x.train[i, 2*j+2] <- as.numeric(bladder0$rtumor[k])
                if(bladder0$rsize[k]!='.')
                    x.train[i, 2*j+3] <- as.numeric(bladder0$rsize[k])
            }
        }
    }
}

pre <- recur.pre.bart(times=times, delta=delta, x.train=x.train)

J <- nrow(pre$tx.train)
for(j in 1:J) {
    if(pre$tx.train[j, 3]>0) {
        pre$tx.train[j, 7] <- pre$tx.train[j, 7+pre$tx.train[j, 3]*2]
        pre$tx.train[j, 8] <- pre$tx.train[j, 8+pre$tx.train[j, 3]*2]
    }
}
pre$tx.train <- pre$tx.train[ , 1:8]

K <- pre$K
NK <- N*K
for(j in 1:NK) {
    if(pre$tx.test[j, 3]>0) {
        pre$tx.test[j, 7] <- pre$tx.test[j, 7+pre$tx.test[j, 3]*2]
        pre$tx.test[j, 8] <- pre$tx.test[j, 8+pre$tx.test[j, 3]*2]
    }
}
pre$tx.test <- pre$tx.test[ , 1:8]


## in bladder1 both number and size are recorded as integers
## from 1 to 8 however they are often missing for recurrences
## at baseline there are no missing and 1 is the mode of both
pre$tx.train[which(is.na(pre$tx.train[ , 7])), 7] <- 1
pre$tx.train[which(is.na(pre$tx.train[ , 8])), 8] <- 1
pre$tx.test[which(is.na(pre$tx.test[ , 7])), 7] <- 1
pre$tx.test[which(is.na(pre$tx.test[ , 8])), 8] <- 1

## it is a good idea to explore more sophisticated methods
## such as imputing the missing data with Sequential BART
## Xu, Daniels and Winterstein.  Sequential BART for imputation of missing
## covariates.  Biostatistics 2016 doi: 10.1093/biostatistics/kxw009
## http://biostatistics.oxfordjournals.org/content/early/2016/03/15/biostatistics.kxw009/suppl/DC1
## https://cran.r-project.org/package=sbart
## library(sbart)
## set.seed(21)
## train <- seqBART(xx=pre$tx.train, yy=NULL, datatype=rep(0, 6),
##                type=0, numskip=20, burn=1000)
## coarsen the imputed data same way as observed example data
## train$imputed5[which(train$imputed5[ , 7]<1), 7] <- 1
## train$imputed5[which(train$imputed5[ , 7]>8), 7] <- 8
## train$imputed5[ , 7] <- round(train$imputed5[ , 7])
## train$imputed5[which(train$imputed5[ , 8]<1), 8] <- 1
## train$imputed5[which(train$imputed5[ , 8]>8), 8] <- 8
## train$imputed5[ , 8] <- round(train$imputed5[ , 8])

## for Friedman's partial dependence, we need to estimate the whole cohort
## at each treatment assignment (and, average over those)
pre$tx.test <- rbind(pre$tx.test, pre$tx.test, pre$tx.test)
pre$tx.test[ , 4] <- c(rep(1, NK), rep(0, 2*NK))          ## Pl
pre$tx.test[ , 5] <- c(rep(0, NK), rep(1, NK), rep(0, NK))## B6
pre$tx.test[ , 6] <- c(rep(0, 2*NK), rep(1, NK))          ## Th

## Not run: 
## set.seed(99)
## post <- recur.bart(y.train=pre$y.train, x.train=pre$tx.train, x.test=pre$tx.test)
## depending on your performance, you may want to run in parallel if available
post <- mc.recur.bart(y.train=pre$y.train, x.train=pre$tx.train,
                      x.test=pre$tx.test, mc.cores=8, seed=99)

M <- nrow(post$yhat.test)
RI.B6.Pl <- matrix(0, nrow=M, ncol=K)
RI.Th.Pl <- matrix(0, nrow=M, ncol=K)
RI.Th.B6 <- matrix(0, nrow=M, ncol=K)

for(j in 1:K) {
    h <- seq(j, NK, K)
    RI.B6.Pl[ , j] <- apply(post$prob.test[ , h+NK]/
                            post$prob.test[ , h], 1, mean)
    RI.Th.Pl[ , j] <- apply(post$prob.test[ , h+2*NK]/
                            post$prob.test[ , h], 1, mean)
    RI.Th.B6[ , j] <- apply(post$prob.test[ , h+2*NK]/
                            post$prob.test[ , h+NK], 1, mean)
}

RI.B6.Pl.mu <- apply(RI.B6.Pl, 2, mean)
RI.B6.Pl.025 <- apply(RI.B6.Pl, 2, quantile, probs=0.025)
RI.B6.Pl.975 <- apply(RI.B6.Pl, 2, quantile, probs=0.975)

RI.Th.Pl.mu <- apply(RI.Th.Pl, 2, mean)
RI.Th.Pl.025 <- apply(RI.Th.Pl, 2, quantile, probs=0.025)
RI.Th.Pl.975 <- apply(RI.Th.Pl, 2, quantile, probs=0.975)

RI.Th.B6.mu <- apply(RI.Th.B6, 2, mean)
RI.Th.B6.025 <- apply(RI.Th.B6, 2, quantile, probs=0.025)
RI.Th.B6.975 <- apply(RI.Th.B6, 2, quantile, probs=0.975)

plot(post$times, RI.Th.Pl.mu, col='blue',
     log='y', main='Bladder cancer ex: Thiotepa vs. Placebo',
     type='l', ylim=c(0.1, 10), ylab='RI(t)', xlab='t (months)')
lines(post$times, RI.Th.Pl.025, col='red')
lines(post$times, RI.Th.Pl.975, col='red')
abline(h=1)

plot(post$times, RI.B6.Pl.mu, col='blue',
     log='y', main='Bladder cancer ex: Vitamin B6 vs. Placebo',
     type='l', ylim=c(0.1, 10), ylab='RI(t)', xlab='t (months)')
lines(post$times, RI.B6.Pl.025, col='red')
lines(post$times, RI.B6.Pl.975, col='red')
abline(h=1)

plot(post$times, RI.Th.B6.mu, col='blue',
     log='y', main='Bladder cancer ex: Thiotepa vs. Vitamin B6',
     type='l', ylim=c(0.1, 10), ylab='RI(t)', xlab='t (months)')
lines(post$times, RI.Th.B6.025, col='red')
lines(post$times, RI.Th.B6.975, col='red')
abline(h=1)


## End(Not run)

BART for dichotomous outcomes with parallel computation and stratified random sampling

Description

BART is a Bayesian “sum-of-trees” model.
For numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .
For a binary response $y$ , $P(Y=1 | x) = F(f(x))$ , where $F$ denotes the standard normal cdf (probit link).

In both cases, $f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

rs.pbart(
   x.train, y.train, x.test=matrix(0.0,0,0),
   C=floor(length(y.train)/2000),
   k=2.0, power=2.0, base=.95,
   binaryOffset=0,
   ntree=50L, numcut=100L,
   ndpost=1000L, nskip=100L,
   keepevery=1L, printevery=100,
   keeptrainfits=FALSE, transposed=FALSE,
   
   mc.cores = 2L, nice = 19L,
   seed = 99L
)
rs.pbart(
   x.train, y.train, x.test=matrix(0.0,0,0),
   C=floor(length(y.train)/2000),
   k=2.0, power=2.0, base=.95,
   binaryOffset=0,
   ntree=50L, numcut=100L,
   ndpost=1000L, nskip=100L,
   keepevery=1L, printevery=100,
   keeptrainfits=FALSE, transposed=FALSE,
   
   mc.cores = 2L, nice = 19L,
   seed = 99L
)

Arguments

`x.train`	Explanatory variables for training (in sample) data. May be a matrix or a data frame, with (as usual) rows corresponding to observations and columns to variables. If a variable is a factor in a data frame, it is replaced with dummies. Note that q dummies are created if q>2 and one dummy is created if q=2, where q is the number of levels of the factor. `pbart` will generate draws of $f(x)$ for each $x$ which is a row of x.train.
`y.train`	Dependent variable for training (in sample) data. If y is numeric a continous response model is fit (normal errors). If y is a factor (or just has values 0 and 1) then a binary response model with a probit link is fit.
`x.test`	Explanatory variables for test (out of sample) data. Should have same structure as x.train. `pbart` will generate draws of $f(x)$ for each $x$ which is a row of x.test.
`C`	The number of shards to break the data into and analyze separately.
`k`	For binary y, k is the number of prior standard deviations $f(x)$ is away from +/-3. In both cases, the bigger k is, the more conservative the fitting will be.
`power`	Power parameter for tree prior.
`base`	Base parameter for tree prior.
`binaryOffset`	Used for binary $y$ . The model is $P(Y=1 \| x) = F(f(x) + binaryOffset)$ . The idea is that $f$ is shrunk towards 0, so the offset allows you to shrink towards a probability other than .5.
`ntree`	The number of trees in the sum.
`numcut`	The number of possible values of c (see usequants). If a single number if given, this is used for all variables. Otherwise a vector with length equal to ncol(x.train) is required, where the $i^{th}$ element gives the number of c used for the $i^{th}$ variable in x.train. If usequants is false, numcut equally spaced cutoffs are used covering the range of values in the corresponding column of x.train. If usequants is true, then min(numcut, the number of unique values in the corresponding columns of x.train - 1) c values are used.
`ndpost`	The number of posterior draws returned.
`nskip`	Number of MCMC iterations to be treated as burn in.
`keepevery`	Every keepevery draw is kept to be returned to the user.
`printevery`	As the MCMC runs, a message is printed every printevery draws.
`keeptrainfits`	Whether to keep `yhat.train` or not.
`transposed`	When running `pbart` in parallel, it is more memory-efficient to transpose `x.train` and `x.test`, if any, prior to calling `mc.pbart`.
`seed`	Setting the seed required for reproducible MCMC.
`mc.cores`	Number of cores to employ in parallel.
`nice`	Set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest).

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case and just $f$ in the binary $y$ case.

Value

rs.pbart returns an object of type pbart which is essentially a list.

`yhat.shard`	Estimates generated from the individual shards rather than from the whole. This object is only useful for assessing convergence. A matrix with ndpost rows and nrow(x.train) columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to a row of x.train. The $(i,j)$ value is $f^(x)$ for the $i^{th}$ kept draw of $f$ and the $j^{th}$ row of x.train. Burn-in is dropped.
`yhat.train`	Estimates generated from the whole if `keeptrainfits=TRUE`. A matrix with ndpost rows and nrow(x.train) columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to a row of x.train. The $(i,j)$ value is $f^(x)$ for the $i^{th}$ kept draw of $f$ and the $j^{th}$ row of x.train. Burn-in is dropped.
`yhat.test`	Estimates generated from the whole if `x.test` is provided. Same as yhat.train but now the x's are the rows of the test data.
`varcount`	a matrix with ndpost rows and nrow(x.train) columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times that variable is used in a tree decision rule (over all trees) is given.

In addition the list has a binaryOffset component giving the value used.

Note that in the binary $y$ , case yhat.train and yhat.test are $f(x)$ + binaryOffset. If you want draws of the probability $P(Y=1 | x)$ you need to apply the normal cdf (pnorm) to these values.

Examples


##simulate from Friedman's five-dimensional test function
##Friedman JH. Multivariate adaptive regression splines
##(with discussion and a rejoinder by the author).
##Annals of Statistics 1991; 19:1-67.

f = function(x) #only the first 5 matter
    sin(pi*x[ , 1]*x[ , 2]) + 2*(x[ , 3]-.5)^2+x[ , 4]+0.5*x[ , 5]-1.5

sigma = 1.0  #y = f(x) + sigma*z where z~N(0, 1)
k = 50       #number of covariates
thin = 25
ndpost = 2500
nskip = 100
C = 10
m = 10
n = 10000

set.seed(12)
x.train=matrix(runif(n*k), n, k)
Ey.train = f(x.train)
y.train=(Ey.train+sigma*rnorm(n)>0)*1
table(y.train)/n

x <- x.train
x4 <- seq(0, 1, length.out=m)

for(i in 1:m) {
    x[ , 4] <- x4[i]

    if(i==1) x.test <- x
    else x.test <- rbind(x.test, x)
}

## parallel::mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
    post = rs.pbart(x.train, y.train, 
                C=C, mc.cores=4, keepevery=1,
                seed=99, ndpost=1, nskip=1)
}

## Not run: 
post = rs.pbart(x.train, y.train, x.test=x.test,
                C=C, mc.cores=8, keepevery=thin,
                seed=99, ndpost=ndpost, nskip=nskip)
str(post)

par(mfrow=c(2, 2))

M <- nrow(post$yhat.test)
pred <- matrix(nrow=M, ncol=10)

for(i in 1:m) {
    h <- (i-1)*n+1:n
    pred[ , i] <- apply(pnorm(post$yhat.test[ , h]), 1, mean)
}

pred <- apply(pred, 2, mean)

plot(x4, qnorm(pred), xlab=expression(x[4]),
     ylab='partial dependence function', type='l')

i <- floor(seq(1, n, length.out=10))
j <- seq(-0.5, 0.4, length.out=10)
for(h in 1:10) {
    auto.corr <- acf(post$yhat.shard[ , i[h]], plot=FALSE)
    if(h==1) {
        max.lag <- max(auto.corr$lag[ , 1, 1])
        plot(1:max.lag+j[h], auto.corr$acf[1+(1:max.lag), 1, 1],
             type='h', xlim=c(0, max.lag+1), ylim=c(-1, 1),
             ylab='auto-correlation', xlab='lag')
    }
    else 
        lines(1:max.lag+j[h], auto.corr$acf[1+(1:max.lag), 1, 1],
              type='h', col=h)
}

for(j in 1:10) {
    if(j==1)
        plot(pnorm(post$yhat.shard[ , i[j]]),
             type='l', ylim=c(0, 1),
             sub=paste0('N:', n, ', k:', k),
             ylab=expression(Phi(f(x))), xlab='m')
    else
        lines(pnorm(post$yhat.shard[ , i[j]]),
              type='l', col=j)
}

geweke <- gewekediag(post$yhat.shard)

j <- -10^(log10(n)-1)
plot(geweke$z, pch='.', cex=2, ylab='z', xlab='i',
     sub=paste0('N:', n, ', k:', k),
     xlim=c(j, n), ylim=c(-5, 5))
lines(1:n, rep(-1.96, n), type='l', col=6)
lines(1:n, rep(+1.96, n), type='l', col=6)
lines(1:n, rep(-2.576, n), type='l', col=5)
lines(1:n, rep(+2.576, n), type='l', col=5)
lines(1:n, rep(-3.291, n), type='l', col=4)
lines(1:n, rep(+3.291, n), type='l', col=4)
lines(1:n, rep(-3.891, n), type='l', col=3)
lines(1:n, rep(+3.891, n), type='l', col=3)
lines(1:n, rep(-4.417, n), type='l', col=2)
lines(1:n, rep(+4.417, n), type='l', col=2)
text(c(1, 1), c(-1.96, 1.96), pos=2, cex=0.6, labels='0.95')
text(c(1, 1), c(-2.576, 2.576), pos=2, cex=0.6, labels='0.99')
text(c(1, 1), c(-3.291, 3.291), pos=2, cex=0.6, labels='0.999')
text(c(1, 1), c(-3.891, 3.891), pos=2, cex=0.6, labels='0.9999')
text(c(1, 1), c(-4.417, 4.417), pos=2, cex=0.6, labels='0.99999')

par(mfrow=c(1, 1))

##dev.copy2pdf(file='geweke.rs.pbart.pdf')

## End(Not run)
##simulate from Friedman's five-dimensional test function
##Friedman JH. Multivariate adaptive regression splines
##(with discussion and a rejoinder by the author).
##Annals of Statistics 1991; 19:1-67.

f = function(x) #only the first 5 matter
    sin(pi*x[ , 1]*x[ , 2]) + 2*(x[ , 3]-.5)^2+x[ , 4]+0.5*x[ , 5]-1.5

sigma = 1.0  #y = f(x) + sigma*z where z~N(0, 1)
k = 50       #number of covariates
thin = 25
ndpost = 2500
nskip = 100
C = 10
m = 10
n = 10000

set.seed(12)
x.train=matrix(runif(n*k), n, k)
Ey.train = f(x.train)
y.train=(Ey.train+sigma*rnorm(n)>0)*1
table(y.train)/n

x <- x.train
x4 <- seq(0, 1, length.out=m)

for(i in 1:m) {
    x[ , 4] <- x4[i]

    if(i==1) x.test <- x
    else x.test <- rbind(x.test, x)
}

## parallel::mcparallel/mccollect do not exist on windows
if(.Platform$OS.type=='unix') {
##test BART with token run to ensure installation works
    post = rs.pbart(x.train, y.train, 
                C=C, mc.cores=4, keepevery=1,
                seed=99, ndpost=1, nskip=1)
}

## Not run: 
post = rs.pbart(x.train, y.train, x.test=x.test,
                C=C, mc.cores=8, keepevery=thin,
                seed=99, ndpost=ndpost, nskip=nskip)
str(post)

par(mfrow=c(2, 2))

M <- nrow(post$yhat.test)
pred <- matrix(nrow=M, ncol=10)

for(i in 1:m) {
    h <- (i-1)*n+1:n
    pred[ , i] <- apply(pnorm(post$yhat.test[ , h]), 1, mean)
}

pred <- apply(pred, 2, mean)

plot(x4, qnorm(pred), xlab=expression(x[4]),
     ylab='partial dependence function', type='l')

i <- floor(seq(1, n, length.out=10))
j <- seq(-0.5, 0.4, length.out=10)
for(h in 1:10) {
    auto.corr <- acf(post$yhat.shard[ , i[h]], plot=FALSE)
    if(h==1) {
        max.lag <- max(auto.corr$lag[ , 1, 1])
        plot(1:max.lag+j[h], auto.corr$acf[1+(1:max.lag), 1, 1],
             type='h', xlim=c(0, max.lag+1), ylim=c(-1, 1),
             ylab='auto-correlation', xlab='lag')
    }
    else 
        lines(1:max.lag+j[h], auto.corr$acf[1+(1:max.lag), 1, 1],
              type='h', col=h)
}

for(j in 1:10) {
    if(j==1)
        plot(pnorm(post$yhat.shard[ , i[j]]),
             type='l', ylim=c(0, 1),
             sub=paste0('N:', n, ', k:', k),
             ylab=expression(Phi(f(x))), xlab='m')
    else
        lines(pnorm(post$yhat.shard[ , i[j]]),
              type='l', col=j)
}

geweke <- gewekediag(post$yhat.shard)

j <- -10^(log10(n)-1)
plot(geweke$z, pch='.', cex=2, ylab='z', xlab='i',
     sub=paste0('N:', n, ', k:', k),
     xlim=c(j, n), ylim=c(-5, 5))
lines(1:n, rep(-1.96, n), type='l', col=6)
lines(1:n, rep(+1.96, n), type='l', col=6)
lines(1:n, rep(-2.576, n), type='l', col=5)
lines(1:n, rep(+2.576, n), type='l', col=5)
lines(1:n, rep(-3.291, n), type='l', col=4)
lines(1:n, rep(+3.291, n), type='l', col=4)
lines(1:n, rep(-3.891, n), type='l', col=3)
lines(1:n, rep(+3.891, n), type='l', col=3)
lines(1:n, rep(-4.417, n), type='l', col=2)
lines(1:n, rep(+4.417, n), type='l', col=2)
text(c(1, 1), c(-1.96, 1.96), pos=2, cex=0.6, labels='0.95')
text(c(1, 1), c(-2.576, 2.576), pos=2, cex=0.6, labels='0.99')
text(c(1, 1), c(-3.291, 3.291), pos=2, cex=0.6, labels='0.999')
text(c(1, 1), c(-3.891, 3.891), pos=2, cex=0.6, labels='0.9999')
text(c(1, 1), c(-4.417, 4.417), pos=2, cex=0.6, labels='0.99999')

par(mfrow=c(1, 1))

##dev.copy2pdf(file='geweke.rs.pbart.pdf')

## End(Not run)

Testing truncated Gamma sampling

Description

Truncated Gamma draws are needed for the standard deviation of the random effects Gibbs conditional.

Usage

rtgamma(n, shape, rate, a)
rtgamma(n, shape, rate, a)

Arguments

`n`	Number of samples.
`shape`	Sampling from a truncated Gamma where $E[x]=shape/rate$ .
`rate`	This parameter is the inverse of the scale which is an alternative representation for the Gamma distribution.
`a`	The truncation point, i.e., $a<x$ .

Value

Returns n truncated Gamma, i.e., $Gam(shape, rate)I(a, infinity)$ .

References

Gentle J. (2013) Random number generation and Monte Carlo methods. Springer, New York, NY.

Examples


set.seed(12)
rtgamma(1, 3, 1, 4)
rtgamma(1, 3, 1, 4)

a=rtgamma(10000, 10, 2, 1)
mean(a)
min(a)

set.seed(12)
rtgamma(1, 3, 1, 4)
rtgamma(1, 3, 1, 4)

a=rtgamma(10000, 10, 2, 1)
mean(a)
min(a)

Testing truncated Normal sampling

Description

Truncated Normal latents are necessary to transform a binary BART into a continuous BART.

Usage

rtnorm(n, mean, sd, tau)
rtnorm(n, mean, sd, tau)

Arguments

`n`	Number of samples.
`mean`	Mean.
`sd`	Standard deviation.
`tau`	Truncation point.

Value

Returns n truncated Normals, i.e., $N(mean, sd)I(tau, infinity)$ .

References

Robert C. (1995) Simulation of truncated normal variables. Statistics and computing, 5(2), 121–125.

Examples


set.seed(12)

rtnorm(1, 0, 1, 3)
rtnorm(1, 0, 1, 3)


set.seed(12)

rtnorm(1, 0, 1, 3)
rtnorm(1, 0, 1, 3)

Estimate spectral density at zero

Description

The spectral density at frequency zero is estimated by fitting an autoregressive model. spectrum0(x)/length(x) estimates the variance of mean(x).

Usage

spectrum0ar(x)
spectrum0ar(x)

Arguments

`x`	Matrix of MCMC chains: the rows are the samples and the columns are different "parameters". For BART, generally, the columns are estimates of $f$ . For `pbart`, they are different subjects. For `surv.bart`, they are different subjects at a grid of times.

Details

The ar() function to fit an autoregressive model to the time series x. For multivariate time series, separate models are fitted for each column. The value of the spectral density at zero is then given by a well-known formula. Adapted from the spectrum0.ar function of the coda package which passes mcmc objects as arguments rather than matrices.

Value

A list with the following values

`spec`	The predicted value of the spectral density at frequency zero.
`order`	The order of the fitted model

References

Martyn Plummer, Nicky Best, Kate Cowles and Karen Vines (2006). CODA: Convergence Diagnosis and Output Analysis for MCMC, R News, vol 6, 7-11.

BW Silverman (1986). Density estimation for statistics and data analysis. Chapman and Hall, London.

Stepwise Variable Selection Procedure for survreg

Description

This stepwise variable selection procedure can be applied to obtain the best candidates for a survreg fit.

Usage

srstepwise(x, times, delta, sle = 0.15, sls = 0.15, dist='lognormal')
srstepwise(x, times, delta, sle = 0.15, sls = 0.15, dist='lognormal')

Arguments

`x`	Matrix of variables to consider.
`times`	The time to an event, if any.
`delta`	The event indicator: 1 for event, 0 for no event.
`sle`	The chosen significance level for entering.
`sls`	The chosen significance level for staying.
`dist`	The distribution to be used by `survreg`.

Details

Unfortunately, no stepwise procedure exists for survreg models. Therefore, we provide this brute force method.

Value

Returns a list of indices of variables which have entered and stayed.

Examples


names. <- names(lung)[-(2:3)]
status1 <- ifelse(lung$status==2,1,0)
X <- as.matrix(lung)[ , names.]
vars=srstepwise(X, lung$time, status1)
print(names.[vars])

names. <- names(lung)[-(2:3)]
status1 <- ifelse(lung$status==2,1,0)
X <- as.matrix(lung)[ , names.]
vars=srstepwise(X, lung$time, status1)
print(names.[vars])

Perform stratified random sampling to balance outcomes

Description

This function is used to perform stratified random sampling to balance outcomes among the shards.

Usage

stratrs(y, C=5, P=0)
stratrs(y, C=5, P=0)

Arguments

`y`	The binary/categorical/continuous outcome.
`C`	The number of shards to break the data set into.
`P`	For continuous data, we break the range into P segments via the quantiles. Specifying, P=20 seems to work reasonably well.

Details

To perform BART with large data sets, random sampling is employed to break the data into C shards. Each shard should be balanced with respect to the outcome. For binary/categorical outcomes, stratified random sampling is employed with this function.

Value

A vector is returned with each element assigned to a shard.

Examples

set.seed(12)
x <- rbinom(25000, 1, 0.1)
a <- stratrs(x)
table(a, x)
z <- pmin(rpois(25000, 0.8), 5)
b <- stratrs(z)
table(b, z)
set.seed(12)
x <- rbinom(25000, 1, 0.1)
a <- stratrs(x)
table(a, x)
z <- pmin(rpois(25000, 0.8), 5)
b <- stratrs(z)
table(b, z)

Survival analysis with BART

Description

We denote the $K$ distinct event/censoring times by $0<t_{(1)}<...<t_{(K)}<\infty$ thus taking $t_{(j)}$ to be the $j^{th}$ order statistic among distinct observation times and, for convenience, $t_{(0)}=0$ . Now consider event indicators $y_{ij}$ for each subject $i$ at each distinct time $t_{(j)}$ up to and including the subject's observation time $t_i=t_{(n_i)}$ with $n_i=\sum_j I[t_{(j)}\leq t_i]$ . This means $y_{ij}=0$ if $j<n_i$ and $y_{in_i}=\delta_i$ .

Usage


surv.bart( x.train=matrix(0,0,0),
           y.train=NULL, times=NULL, delta=NULL,
           x.test=matrix(0,0,0),
           K=NULL, events=NULL, ztimes=NULL, zdelta=NULL,
           sparse=FALSE, theta=0, omega=1,
           a=0.5, b=1, augment=FALSE, rho=NULL,
           xinfo=matrix(0,0,0), usequants=FALSE,
           
           rm.const=TRUE, type='pbart',
           ntype=as.integer(
               factor(type, levels=c('wbart', 'pbart', 'lbart'))),
           k=2, power=2, base=.95,
           offset=NULL, tau.num=c(NA, 3, 6)[ntype], 
           ntree=50, numcut=100, ndpost=1000, nskip=250,
           keepevery = 10L,
           
           
           
           
           printevery=100L, 
           
           id=NULL,    ## surv.bart only
           seed=99,    ## mc.surv.bart only
           mc.cores=2, ## mc.surv.bart only
           nice=19L    ## mc.surv.bart only
         )

mc.surv.bart( x.train=matrix(0,0,0),
              y.train=NULL, times=NULL, delta=NULL, 
              x.test=matrix(0,0,0), 
              K=NULL, events=NULL, ztimes=NULL, zdelta=NULL,
              sparse=FALSE, theta=0, omega=1,
              a=0.5, b=1, augment=FALSE, rho=NULL,
              xinfo=matrix(0,0,0), usequants=FALSE,
              
              rm.const=TRUE, type='pbart',
              ntype=as.integer(
                  factor(type, levels=c('wbart', 'pbart', 'lbart'))),
              k=2, power=2, base=.95,
              offset=NULL, tau.num=c(NA, 3, 6)[ntype], 
              ntree=50, numcut=100, ndpost=1000, nskip=250,
              keepevery = 10L,
              
              
              
              
              printevery=100L, 
              
              id=NULL,    ## surv.bart only
              seed=99,    ## mc.surv.bart only
              mc.cores=2, ## mc.surv.bart only
              nice=19L    ## mc.surv.bart only
            )
surv.bart( x.train=matrix(0,0,0),
           y.train=NULL, times=NULL, delta=NULL,
           x.test=matrix(0,0,0),
           K=NULL, events=NULL, ztimes=NULL, zdelta=NULL,
           sparse=FALSE, theta=0, omega=1,
           a=0.5, b=1, augment=FALSE, rho=NULL,
           xinfo=matrix(0,0,0), usequants=FALSE,
           
           rm.const=TRUE, type='pbart',
           ntype=as.integer(
               factor(type, levels=c('wbart', 'pbart', 'lbart'))),
           k=2, power=2, base=.95,
           offset=NULL, tau.num=c(NA, 3, 6)[ntype], 
           ntree=50, numcut=100, ndpost=1000, nskip=250,
           keepevery = 10L,
           
           
           
           
           printevery=100L, 
           
           id=NULL,    ## surv.bart only
           seed=99,    ## mc.surv.bart only
           mc.cores=2, ## mc.surv.bart only
           nice=19L    ## mc.surv.bart only
         )

mc.surv.bart( x.train=matrix(0,0,0),
              y.train=NULL, times=NULL, delta=NULL, 
              x.test=matrix(0,0,0), 
              K=NULL, events=NULL, ztimes=NULL, zdelta=NULL,
              sparse=FALSE, theta=0, omega=1,
              a=0.5, b=1, augment=FALSE, rho=NULL,
              xinfo=matrix(0,0,0), usequants=FALSE,
              
              rm.const=TRUE, type='pbart',
              ntype=as.integer(
                  factor(type, levels=c('wbart', 'pbart', 'lbart'))),
              k=2, power=2, base=.95,
              offset=NULL, tau.num=c(NA, 3, 6)[ntype], 
              ntree=50, numcut=100, ndpost=1000, nskip=250,
              keepevery = 10L,
              
              
              
              
              printevery=100L, 
              
              id=NULL,    ## surv.bart only
              seed=99,    ## mc.surv.bart only
              mc.cores=2, ## mc.surv.bart only
              nice=19L    ## mc.surv.bart only
            )

Arguments

`x.train`	Explanatory variables for training (in sample) data. Must be a matrix with (as usual) rows corresponding to observations and columns to variables. `surv.bart` will generate draws of $f(t, x)$ for each $x$ which is a row of x.train (note that the definition of `x.train` is dependent on whether `y.train` has been specified; see below).
`y.train`	Binary response dependent variable for training (in sample) data. If `y.train` is `NULL`, then `y.train` (`x.train` and `x.test`, if specified) are generated by a call to `surv.pre.bart` (which require that `times` and `delta` be provided: see below); otherwise, `y.train` (`x.train` and `x.test`, if specified) are utilized as given assuming that the data construction has already been performed.
`times`	The time of event or right-censoring. If `y.train` is `NULL`, then `times` (and `delta`) must be provided.
`delta`	The event indicator: 1 is an event while 0 is censored. If `y.train` is `NULL`, then `delta` (and `times`) must be provided.
`x.test`	Explanatory variables for test (out of sample) data. Must be a matrix and have the same structure as x.train. `surv.bart` will generate draws of $f(t, x)$ for each $x$ which is a row of x.test.
`K`	If provided, then coarsen `times` per the quantiles $1/K, 2/K, ..., K/K$ .
`events`	If provided, then use for the grid of time points.
`ztimes`	If provided, then these columns of `x.train` (and `x.test` if any) are the times for time-dependent covariates. They will be transformed into time-dependent covariate sojourn times.
`zdelta`	If provided, then these columns of `x.train` (and `x.test` if any) are the delta for time-dependent covariates. They will be transformed into time-dependent covariate binary events.
`sparse`	Whether to perform variable selection based on a sparse Dirichlet prior rather than simply uniform; see Linero 2016.
`theta`	Set $theta$ parameter; zero means random.
`omega`	Set $omega$ parameter; zero means random.
`a`	Sparse parameter for $Beta(a, b)$ prior: $0.5<=a<=1$ where lower values inducing more sparsity.
`b`	Sparse parameter for $Beta(a, b)$ prior; typically, $b=1$ .
`rho`	Sparse parameter: typically $rho=p$ where $p$ is the number of covariates under consideration.
`augment`	Whether data augmentation is to be performed in sparse variable selection.
`xinfo`	You can provide the cutpoints to BART or let BART choose them for you. To provide them, use the `xinfo` argument to specify a list (matrix) where the items (rows) are the covariates and the contents of the items (columns) are the cutpoints.
`usequants`	If `usequants=FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, uniform quantiles are used for the cutpoints.
`rm.const`	Whether or not to remove constant variables.
`type`	Whether to employ Albert-Chib, `'pbart'`, or Holmes-Held, `'lbart'`.
`ntype`	The integer equivalent of `type` where `'wbart'` is 1, `'pbart'` is 2 and `'lbart'` is 3.
`k`	k is the number of prior standard deviations $f(t, x)$ is away from +/-3. The bigger k is, the more conservative the fitting will be.
`power`	Power parameter for tree prior.
`base`	Base parameter for tree prior.
`offset`	With binary BART, the centering is $P(Y=1 \| x) = F(f(x) + offset)$ where `offset` defaults to `F^{-1}(mean(y.train))`. You can use the `offset` parameter to over-ride these defaults.
`tau.num`	The numerator in the `tau` definition, i.e., `tau=tau.num/(k*sqrt(ntree))`.
`ntree`	The number of trees in the sum.
`ndpost`	The number of posterior draws returned.
`nskip`	Number of MCMC iterations to be treated as burn in.
`printevery`	As the MCMC runs, a message is printed every printevery draws.
`keepevery`	Every keepevery draw is kept to be returned to the user. A “draw” will consist of values $f^(t, x)$ at $x$ = rows from the train(optionally) and test data, where $f^$ denotes the current draw of $f$ .
`numcut`	The number of possible values of c (see usequants). If a single number if given, this is used for all variables. Otherwise a vector with length equal to ncol(x.train) is required, where the $i^{th}$ element gives the number of c used for the $i^{th}$ variable in x.train. If usequants is false, numcut equally spaced cutoffs are used covering the range of values in the corresponding column of x.train. If usequants is true, then min(numcut, the number of unique values in the corresponding columns of x.train - 1) c values are used.
`id`	`surv.bart` only: unique identifier added to returned list.
`seed`	`mc.surv.bart` only: seed required for reproducible MCMC.
`mc.cores`	`mc.surv.bart` only: number of cores to employ in parallel.
`nice`	`mc.surv.bart` only: set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest).

Value

surv.bart returns an object of type survbart which is essentially a list. Besides the items listed below, the list has a binaryOffset component giving the value used, a times component giving the unique times, K which is the number of unique times, tx.train and tx.test, if any.

`yhat.train`	A matrix with ndpost rows and nrow(x.train) columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to a row of x.train. The $(i,j)$ value is $f^(t, x)$ for the $i^{th}$ kept draw of $f$ and the $j^{th}$ row of x.train. Burn-in is dropped.
`yhat.test`	Same as yhat.train but now the x's are the rows of the test data.
`surv.test`	The survival function, $S(t\|x)$ , where x's are the rows of the test data.
`yhat.train.mean`	train data fits = mean of yhat.train columns.
`yhat.test.mean`	test data fits = mean of yhat.test columns.
`surv.test.mean`	mean of surv.test columns.
`varcount`	a matrix with ndpost rows and nrow(x.train) columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times that variable is used in a tree decision rule (over all trees) is given.

Note that yhat.train and yhat.test are $f(t, x)$ + binaryOffset. If you want draws of the probability $P(Y=1 | t, x)$ you need to apply the normal cdf (pnorm) to these values.

Examples


## load survival package for the advanced lung cancer example
data(lung)

N <- length(lung$status)

table(lung$ph.karno, lung$pat.karno)

## if physician's KPS unavailable, then use the patient's
h <- which(is.na(lung$ph.karno))
lung$ph.karno[h] <- lung$pat.karno[h]

times <- lung$time
delta <- lung$status-1 ##lung$status: 1=censored, 2=dead
##delta: 0=censored, 1=dead

## this study reports time in days rather than weeks or months
## coarsening from days to weeks or months will reduce the computational burden
##times <- ceiling(times/30)
times <- ceiling(times/7)  ## weeks

table(times)
table(delta)

## matrix of observed covariates
x.train <- cbind(lung$sex, lung$age, lung$ph.karno)

## lung$sex:        Male=1 Female=2
## lung$age:        Age in years
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('M(1):F(2)', 'age(39:82)', 'ph.karno(50:100:10)')

table(x.train[ , 1])
summary(x.train[ , 2])
table(x.train[ , 3])

##test BART with token run to ensure installation works
set.seed(99)
post <- surv.bart(x.train=x.train, times=times, delta=delta,
                  nskip=1, ndpost=1, keepevery=1)

## Not run: 
## run one long MCMC chain in one process
## set.seed(99)
## post <- surv.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

## in the interest of time, consider speeding it up by parallel processing
## run "mc.cores" number of shorter MCMC chains in parallel processes
post <- mc.surv.bart(x.train=x.train, times=times, delta=delta,
                     mc.cores=8, seed=99)

pre <- surv.pre.bart(times=times, delta=delta, x.train=x.train,
                     x.test=x.train)

K <- pre$K
M <- nrow(post$yhat.train)

pre$tx.test <- rbind(pre$tx.test, pre$tx.test)
pre$tx.test[ , 2] <- c(rep(1, N*K), rep(2, N*K))
## sex pushed to col 2, since time is always in col 1

pred <- predict(post, newdata=pre$tx.test, mc.cores=8)

pd <- matrix(nrow=M, ncol=2*K)

for(j in 1:K) {
    h <- seq(j, N*K, by=K)
    pd[ , j] <- apply(pred$surv.test[ , h], 1, mean)
    pd[ , j+K] <- apply(pred$surv.test[ , h+N*K], 1, mean)
}

pd.mu  <- apply(pd, 2, mean)
pd.025 <- apply(pd, 2, quantile, probs=0.025)
pd.975 <- apply(pd, 2, quantile, probs=0.975)

males <- 1:K
females <- males+K

plot(c(0, pre$times), c(1, pd.mu[males]), type='s', col='blue',
     ylim=0:1, ylab='S(t, x)', xlab='t (weeks)',
     main=paste('Advanced Lung Cancer ex. (BART::lung)',
                "Friedman's partial dependence function",
                'Male (blue) vs. Female (red)', sep='\n'))
lines(c(0, pre$times), c(1, pd.025[males]), col='blue', type='s', lty=2)
lines(c(0, pre$times), c(1, pd.975[males]), col='blue', type='s', lty=2)
lines(c(0, pre$times), c(1, pd.mu[females]), col='red', type='s')
lines(c(0, pre$times), c(1, pd.025[females]), col='red', type='s', lty=2)
lines(c(0, pre$times), c(1, pd.975[females]), col='red', type='s', lty=2)



## End(Not run)
## load survival package for the advanced lung cancer example
data(lung)

N <- length(lung$status)

table(lung$ph.karno, lung$pat.karno)

## if physician's KPS unavailable, then use the patient's
h <- which(is.na(lung$ph.karno))
lung$ph.karno[h] <- lung$pat.karno[h]

times <- lung$time
delta <- lung$status-1 ##lung$status: 1=censored, 2=dead
##delta: 0=censored, 1=dead

## this study reports time in days rather than weeks or months
## coarsening from days to weeks or months will reduce the computational burden
##times <- ceiling(times/30)
times <- ceiling(times/7)  ## weeks

table(times)
table(delta)

## matrix of observed covariates
x.train <- cbind(lung$sex, lung$age, lung$ph.karno)

## lung$sex:        Male=1 Female=2
## lung$age:        Age in years
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('M(1):F(2)', 'age(39:82)', 'ph.karno(50:100:10)')

table(x.train[ , 1])
summary(x.train[ , 2])
table(x.train[ , 3])

##test BART with token run to ensure installation works
set.seed(99)
post <- surv.bart(x.train=x.train, times=times, delta=delta,
                  nskip=1, ndpost=1, keepevery=1)

## Not run: 
## run one long MCMC chain in one process
## set.seed(99)
## post <- surv.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)

## in the interest of time, consider speeding it up by parallel processing
## run "mc.cores" number of shorter MCMC chains in parallel processes
post <- mc.surv.bart(x.train=x.train, times=times, delta=delta,
                     mc.cores=8, seed=99)

pre <- surv.pre.bart(times=times, delta=delta, x.train=x.train,
                     x.test=x.train)

K <- pre$K
M <- nrow(post$yhat.train)

pre$tx.test <- rbind(pre$tx.test, pre$tx.test)
pre$tx.test[ , 2] <- c(rep(1, N*K), rep(2, N*K))
## sex pushed to col 2, since time is always in col 1

pred <- predict(post, newdata=pre$tx.test, mc.cores=8)

pd <- matrix(nrow=M, ncol=2*K)

for(j in 1:K) {
    h <- seq(j, N*K, by=K)
    pd[ , j] <- apply(pred$surv.test[ , h], 1, mean)
    pd[ , j+K] <- apply(pred$surv.test[ , h+N*K], 1, mean)
}

pd.mu  <- apply(pd, 2, mean)
pd.025 <- apply(pd, 2, quantile, probs=0.025)
pd.975 <- apply(pd, 2, quantile, probs=0.975)

males <- 1:K
females <- males+K

plot(c(0, pre$times), c(1, pd.mu[males]), type='s', col='blue',
     ylim=0:1, ylab='S(t, x)', xlab='t (weeks)',
     main=paste('Advanced Lung Cancer ex. (BART::lung)',
                "Friedman's partial dependence function",
                'Male (blue) vs. Female (red)', sep='\n'))
lines(c(0, pre$times), c(1, pd.025[males]), col='blue', type='s', lty=2)
lines(c(0, pre$times), c(1, pd.975[males]), col='blue', type='s', lty=2)
lines(c(0, pre$times), c(1, pd.mu[females]), col='red', type='s')
lines(c(0, pre$times), c(1, pd.025[females]), col='red', type='s', lty=2)
lines(c(0, pre$times), c(1, pd.975[females]), col='red', type='s', lty=2)



## End(Not run)

Data construction for survival analysis with BART

Description

Survival data contained in $(t,\delta, x)$ must be translated to data suitable for the BART survival analysis model; see surv.bart for more details.

Usage

surv.pre.bart( times, delta, x.train=NULL, x.test=NULL,
               K=NULL, events=NULL, ztimes=NULL, zdelta=NULL )
surv.pre.bart( times, delta, x.train=NULL, x.test=NULL,
               K=NULL, events=NULL, ztimes=NULL, zdelta=NULL )

Arguments

`times`	The time of event or right-censoring.
`delta`	The event indicator: 1 is an event while 0 is censored.
`x.train`	Explanatory variables for training (in sample) data. If provided, must be a matrix with (as usual) rows corresponding to observations and columns to variables.
`x.test`	Explanatory variables for test (out of sample) data. If provided, must be a matrix and have the same structure as x.train.
`K`	If provided, then coarsen `times` per the quantiles $1/K, 2/K, ..., K/K$ .
`events`	If provided, then use for the grid of time points.
`ztimes`	If provided, then these columns of `x.train` (and `x.test` if any) are the times for time-dependent covariates. They will be transformed into time-dependent covariate sojourn times.
`zdelta`	If provided, then these columns of `x.train` (and `x.test` if any) are the delta for time-dependent covariates. They will be transformed into time-dependent covariate binary events.

Value

surv.pre.bart returns a list. Besides the items listed below, the list has a times component giving the unique times and K which is the number of unique times.

`y.train`	A vector of binary responses.
`tx.train`	A matrix with rows consisting of time and the covariates of the training data.
`tx.test`	A matrix with rows consisting of time and the covariates of the test data, if any.

Examples


## load the advanced lung cancer example
data(lung)

group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno
times <- lung[group, 2]   ##lung$time
delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead
                          ##delta: 0=censored, 1=dead

summary(times)
table(delta)

x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates
## lung$age:        Age in years
## lung$sex:        Male=1 Female=2
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)')

summary(x.train[ , 1])
table(x.train[ , 2])
table(x.train[ , 3])

x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios

dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

i <- 1

for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) {
    x.test[i, ] <- c(age, sex, ph.karno)
    i <- i+1
}

pre <- surv.pre.bart(times=times, delta=delta, x.train=x.train, x.test=x.test)
str(pre)
    
## load the advanced lung cancer example
data(lung)

group <- -which(is.na(lung[ , 7])) ## remove missing row for ph.karno
times <- lung[group, 2]   ##lung$time
delta <- lung[group, 3]-1 ##lung$status: 1=censored, 2=dead
                          ##delta: 0=censored, 1=dead

summary(times)
table(delta)

x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates
## lung$age:        Age in years
## lung$sex:        Male=1 Female=2
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)')

summary(x.train[ , 1])
table(x.train[ , 2])
table(x.train[ , 3])

x.test <- matrix(nrow=84, ncol=3) ## matrix of covariate scenarios

dimnames(x.test)[[2]] <- dimnames(x.train)[[2]]

i <- 1

for(age in 5*(9:15)) for(sex in 1:2) for(ph.karno in 10*(5:10)) {
    x.test[i, ] <- c(age, sex, ph.karno)
    i <- i+1
}

pre <- surv.pre.bart(times=times, delta=delta, x.train=x.train, x.test=x.test)
str(pre)

Liver transplant waiting list

Description

Subjects on a liver transplant waiting list from 1990-1999, and their disposition: received a transplant, died while waiting, withdrew from the list, or censored.

Usage

data("transplant")data("transplant")

Format

A data frame with 815 observations on the following 6 variables.

age: age at addition to the waiting list
sex: m or f
abo: blood type: A, B, AB or O
year: year in which they entered the waiting list
futime: time from entry to final disposition
event: final disposition: censored, death, ltx or withdraw

Details

This represents the transplant experience in a particular region, over a time period in which liver transplant became much more widely recognized as a viable treatment modality. The number of liver transplants rises over the period, but the number of subjects added to the liver transplant waiting list grew much faster. Important questions addressed by the data are the change in waiting time, who waits, and whether there was an consequent increase in deaths while on the list.

Blood type is an important consideration. Donor livers from subjects with blood type O can be used by patients with A, B, AB or O blood types, whereas a donor liver from the other types will only be transplanted to a matching recipient. Thus type O subjects on the waiting list are at a disadvantage, since the pool of competitors is larger for type O donor livers.

This data is of historical interest and provides a useful example of competing risks, but it has little relevance to current practice. Liver allocation policies have evolved and now depend directly on each individual patient's risk and need, assessments of which are regularly updated while a patient is on the waiting list. The overall organ shortage remains acute, however.

References

Kim WR, Therneau TM, Benson JT, Kremers WK, Rosen CB, Gores GJ, Dickson ER. Deaths on the liver transplant waiting list: An analysis of competing risks. Hepatology 2006 Feb; 43(2):345-51.

BART for continuous outcomes

Description

BART is a Bayesian “sum-of-trees” model.
For a numeric response $y$ , we have $y = f(x) + \epsilon$ , where $\epsilon \sim N(0,\sigma^2)$ .

$f$ is the sum of many tree models. The goal is to have very flexible inference for the uknown function $f$ .

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

Usage

wbart(
   x.train, y.train, x.test=matrix(0.0,0,0),
   sparse=FALSE, theta=0, omega=1,
   a=0.5, b=1, augment=FALSE, rho=NULL,
   xinfo=matrix(0.0,0,0), usequants=FALSE,
   cont=FALSE, rm.const=TRUE,
   sigest=NA, sigdf=3, sigquant=.90,
   k=2.0, power=2.0, base=.95,
   sigmaf=NA, lambda=NA,
   fmean=mean(y.train), w=rep(1,length(y.train)),
   ntree=200L, numcut=100L,
   ndpost=1000L, nskip=100L, keepevery=1L,
   nkeeptrain=ndpost, nkeeptest=ndpost,
   nkeeptestmean=ndpost, nkeeptreedraws=ndpost,
   printevery=100L, transposed=FALSE 
)
wbart(
   x.train, y.train, x.test=matrix(0.0,0,0),
   sparse=FALSE, theta=0, omega=1,
   a=0.5, b=1, augment=FALSE, rho=NULL,
   xinfo=matrix(0.0,0,0), usequants=FALSE,
   cont=FALSE, rm.const=TRUE,
   sigest=NA, sigdf=3, sigquant=.90,
   k=2.0, power=2.0, base=.95,
   sigmaf=NA, lambda=NA,
   fmean=mean(y.train), w=rep(1,length(y.train)),
   ntree=200L, numcut=100L,
   ndpost=1000L, nskip=100L, keepevery=1L,
   nkeeptrain=ndpost, nkeeptest=ndpost,
   nkeeptestmean=ndpost, nkeeptreedraws=ndpost,
   printevery=100L, transposed=FALSE 
)

Arguments

`x.train`	Explanatory variables for training (in sample) data. May be a matrix or a data frame, with (as usual) rows corresponding to observations and columns to variables. If a variable is a factor in a data frame, it is replaced with dummies. Note that q dummies are created if q>2 and one dummy is created if q=2, where q is the number of levels of the factor. `wbart` will generate draws of $f(x)$ for each $x$ which is a row of `x.train`.
`y.train`	Continuous dependent variable for training (in sample) data.
`x.test`	Explanatory variables for test (out of sample) data. Should have same structure as x.train. `wbart` will generate draws of $f(x)$ for each $x$ which is a row of x.test.
`sparse`	Whether to perform variable selection based on a sparse Dirichlet prior rather than simply uniform; see Linero 2016.
`theta`	Set $theta$ parameter; zero means random.
`omega`	Set $omega$ parameter; zero means random.
`a`	Sparse parameter for $Beta(a, b)$ prior: $0.5<=a<=1$ where lower values inducing more sparsity.
`b`	Sparse parameter for $Beta(a, b)$ prior; typically, $b=1$ .
`rho`	Sparse parameter: typically $rho=p$ where $p$ is the number of covariates under consideration.
`augment`	Whether data augmentation is to be performed in sparse variable selection.
`xinfo`	You can provide the cutpoints to BART or let BART choose them for you. To provide them, use the `xinfo` argument to specify a list (matrix) where the items (rows) are the covariates and the contents of the items (columns) are the cutpoints.
`usequants`	If `usequants=FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, uniform quantiles are used for the cutpoints.
`cont`	Whether or not to assume all variables are continuous.
`rm.const`	Whether or not to remove constant variables.
`sigest`	The prior for the error variance ( $\sigma^2$ ) is inverted chi-squared (the standard conditionally conjugate prior). The prior is specified by choosing the degrees of freedom, a rough estimate of the corresponding standard deviation and a quantile to put this rough estimate at. If sigest=NA then the rough estimate will be the usual least squares estimator. Otherwise the supplied value will be used.
`sigdf`	Degrees of freedom for error variance prior.
`sigquant`	The quantile of the prior that the rough estimate (see sigest) is placed at. The closer the quantile is to 1, the more aggresive the fit will be as you are putting more prior weight on error standard deviations ( $\sigma$ ) less than the rough estimate.
`k`	For numeric y, k is the number of prior standard deviations $E(Y\|x) = f(x)$ is away from +/-.5. k is the number of prior standard deviations $f(x)$ is away from +/-3. The bigger k is, the more conservative the fitting will be.
`power`	Power parameter for tree prior.
`base`	Base parameter for tree prior.
`sigmaf`	The SD of f.
`lambda`	The scale of the prior for the variance.
`fmean`	BART operates on `y.train` centered by `fmean`.
`w`	Vector of weights which multiply the standard deviation.
`ntree`	The number of trees in the sum.
`numcut`	The number of possible values of c (see usequants). If a single number if given, this is used for all variables. Otherwise a vector with length equal to ncol(x.train) is required, where the $i^{th}$ element gives the number of c used for the $i^{th}$ variable in x.train. If usequants is false, numcut equally spaced cutoffs are used covering the range of values in the corresponding column of x.train. If usequants is true, then min(numcut, the number of unique values in the corresponding columns of x.train - 1) c values are used.
`ndpost`	The number of posterior draws returned.
`nskip`	Number of MCMC iterations to be treated as burn in.
`nkeeptrain`	Number of MCMC iterations to be returned for train data.
`nkeeptest`	Number of MCMC iterations to be returned for test data.
`nkeeptestmean`	Number of MCMC iterations to be returned for test mean.
`nkeeptreedraws`	Number of MCMC iterations to be returned for tree draws.
`printevery`	As the MCMC runs, a message is printed every printevery draws.
`keepevery`	Every keepevery draw is kept to be returned to the user.
`transposed`	When running `wbart` in parallel, it is more memory-efficient to transpose `x.train` and `x.test`, if any, prior to calling `mc.wbart`.

Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior $(f,\sigma) | (x,y)$ in the numeric $y$ case.

Value

wbart returns an object of type wbart which is essentially a list. In the numeric $y$ case, the list has components:

`yhat.train`	A matrix with ndpost rows and nrow(x.train) columns. Each row corresponds to a draw $f^$ from the posterior of $f$ and each column corresponds to a row of x.train. The $(i,j)$ value is $f^(x)$ for the $i^{th}$ kept draw of $f$ and the $j^{th}$ row of x.train. Burn-in is dropped.
`yhat.test`	Same as yhat.train but now the x's are the rows of the test data.
`yhat.train.mean`	train data fits = mean of yhat.train columns.
`yhat.test.mean`	test data fits = mean of yhat.test columns.
`sigma`	post burn in draws of sigma, length = ndpost.
`first.sigma`	burn-in draws of sigma.
`varcount`	a matrix with ndpost rows and nrow(x.train) columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times that variable is used in a tree decision rule (over all trees) is given.
`sigest`	The rough error standard deviation ( $\sigma$ ) used in the prior.

Examples

##simulate data (example from Friedman MARS paper)
f = function(x){
10*sin(pi*x[,1]*x[,2]) + 20*(x[,3]-.5)^2+10*x[,4]+5*x[,5]
}
sigma = 1.0  #y = f(x) + sigma*z , z~N(0,1)
n = 100      #number of observations
set.seed(99)
x=matrix(runif(n*10),n,10) #10 variables, only first 5 matter
Ey = f(x)
y=Ey+sigma*rnorm(n)
lmFit = lm(y~.,data.frame(x,y)) #compare lm fit to BART later

##test BART with token run to ensure installation works
set.seed(99)
bartFit = wbart(x,y,nskip=5,ndpost=5)

## Not run: 
##run BART
set.seed(99)
bartFit = wbart(x,y)

##compare BART fit to linear matter and truth = Ey
fitmat = cbind(y,Ey,lmFit$fitted,bartFit$yhat.train.mean)
colnames(fitmat) = c('y','Ey','lm','bart')
print(cor(fitmat))

## End(Not run)
##simulate data (example from Friedman MARS paper)
f = function(x){
10*sin(pi*x[,1]*x[,2]) + 20*(x[,3]-.5)^2+10*x[,4]+5*x[,5]
}
sigma = 1.0  #y = f(x) + sigma*z , z~N(0,1)
n = 100      #number of observations
set.seed(99)
x=matrix(runif(n*10),n,10) #10 variables, only first 5 matter
Ey = f(x)
y=Ey+sigma*rnorm(n)
lmFit = lm(y~.,data.frame(x,y)) #compare lm fit to BART later

##test BART with token run to ensure installation works
set.seed(99)
bartFit = wbart(x,y,nskip=5,ndpost=5)

## Not run: 
##run BART
set.seed(99)
bartFit = wbart(x,y)

##compare BART fit to linear matter and truth = Ey
fitmat = cbind(y,Ey,lmFit$fitted,bartFit$yhat.train.mean)
colnames(fitmat) = c('y','Ey','lm','bart')
print(cor(fitmat))

## End(Not run)

A data set used in example of `recur.bart`.

Description

A matrix containing a 20% random sample of the testing set for a real data example of recurrent events survival analysis. There are 100 patients in the cohort: 50 in the training set and 50 in the testing set. See the Reference below (and the References therein) for more detailed information; a brief synopsis follows.

xdm20.test contains both the training set and the testing set. There are 798 unique time points so there are 50*798=39900 rows of the training set followed by 50*798=39900 rows of the testing set. For patient's who died prior to the end of follow-up, their external factors are last value carried forward. Therefore, we can use xdm20.test to estimate the cumulative hazard for all patients for all time points.

The full data set, xdm.test, can be obtained online at https://www.mcw.edu/-/media/MCW/Departments/Biostatistics/tr064zip.zip There are 488 patients in the full cohort: 235 in the training set and 253 in the testing set.

xdm.test contains both the training set and the testing set. There are 798 unique time points so there are 235*798=187530 rows of the training set followed by 253*798=201894 rows of the testing set. For patient's who died prior to the end of follow-up, their external factors are last value carried forward.

Usage

data(xdm20.test)data(xdm20.test)

References

Sparapani, Rein, Tarima, Jackson, Meurer (2020). Non-parametric recurrent events analysis with BART and an application to the hospital admissions of patients with diabetes. Biostatistics doi:10.1093/biostatistics/kxy032

Examples

data(xdm20.test)
head(xdm20.test[ , 1:10])
data(xdm20.test)
head(xdm20.test[ , 1:10])

A real data example for `recur.bart`.

Description

A matrix containing a 20% random sample of the training set for a real data example of recurrent events survival analysis. There are 100 patients in the cohort: 50 in the training set and 50 in the testing set. The full data set, xdm.train, can be obtained online at https://www.mcw.edu/-/media/MCW/Departments/Biostatistics/tr064zip.zip There are 488 patients in the full cohort: 235 in the training set and 253 in the testing set. See the Reference below (and the References therein) for more detailed information; a brief synopsis follows.

We explored the hospital admissions for a cohort of patients with diabetes cared for by the Froedtert and Medical College of Wisconsin health network. These patients were identified via their Electronic Health Records (EHR) which include vital signs, diagnoses, procedures, laboratory values, pharmacy orders and billing data. This human subjects research and de-identified data release was approved by the Medical College of Wisconsin and Froedtert Hospital joint Institutional Review Board. To maintain patient privacy, roughly one fourth of patients were randomly sampled for inclusion as well as other de-identification procedures.

We identified likely incident diabetes mellitus type 2 patients by tabulating their first diagnosis code of primary diabetes (ICD-9 codes 250.x0 and 250.x2) in 2006 or 2007, i.e., no such codes were found for these patients prior to 2006 for as far back as each patient's records go which is variable. We restricted the population to adults aged 21 to 90 by 01/01/2008. Among the patients treated in this health system, the vast majority were racially self-identified as either white or black so our inclusion criteria is restricted to these groups. Since our interest is in patients with primary diabetes, we excluded those patients who were diagnosed with either secondary diabetes or gestational diabetes.

For this cohort, we identified every hospital admission between 01/01/2008 and 12/31/2012. For convenience, follow-up begins on 01/01/2008, rather than from each patient's actual incident diagnosis date which varied over the preceding 2 years. Following all patients concurrently allows us to temporally adapt, via our model, for seasonal/epidemic hospital admissions such as the H1N1 influenza outbreak in the US from April to June 2009.

We investigated the following risk factors: gender, race, age, insurance status (commercial, government or other), diabetes therapy (insulin, metformin and/or sulfonylurea), health care charges, relative value units (RVU), vital signs, laboratory values, comorbidity/complication diagnoses and procedures/surgeries (we will refer to vital signs and laboratory values collectively as signs; and comorbidity/complication diagnoses and procedures/surgeries collectively as conditions). In total, we considered 85 covariates of which 82 are external factors as described above and three are temporal factors: time, $t$ , the counting process, $N_i(t-)$ , and the sojourn time, $v_i(t)$ . Among these potential predictors only gender, race and age are time-independent. The rest are defined as last value carried forward.

For insulin, metformin and sulfonylurea, we only had access to prescription orders (rather than prescription fills) and self-reported current status of prescription therapy during clinic office visits. Since, generally, orders are only required after every three fills, and each fill can be for up to 90 days, we define insulin, metformin and sulfonylurea as binary indicators which are one if there exists an order or current status indication within the prior 270 days; otherwise zero.

Health care charges and relative value units (RVU) are measures related to the services and procedures delivered. However, they are so closely related that recent charges/RVUs are of no practical value in this analysis. For example, just prior to a patient's hospital admission on a non-emergent basis, they often have a series of diagnostic tests and imaging. Similarly, for an emergent admission, the patient is often seen in the emergency department just prior to admission where similar services are conducted. We do not consider these charges/RVUs predictive of an admission because we are interested in identifying preventive opportunities. Therefore, we investigate charges/RVUs that are the sum total of the following moving windows of days prior to any given date: 31 to 90, 91 to 180, 181 to 300.

For many patients, some signs were not available for a given date so they were imputed; similarly, if a sign was not observed within the last 180 days, then it was imputed (except for height which never expires, weight extended to 365 days and body mass index which is a deterministic function of the two). We utilized the Sequential BART missing imputation method. However, instead of creating several imputed data sets, we imputed a new sign at each date when it was missing, i.e., in order to properly address uncertainty within one data set, a new value was imputed for each date that it was missing and never carried forward.

Conditions are binary indicators which are zero until the date of the first coding and then they are one from then on. Based on clinical rationale, we identified 26 conditions (23 comorbidities and 3 procedures/surgeries) which are potential risk factors for a hospital admission many of which are possible complications of diabetes; besides clinical merit, these conditions were chosen since they are present in more than just a few subjects so that they may be informative. Similarly, we employed 15 general conditions which are the Charlson diagnoses and 18 general conditions from the RxRisk adult diagnoses which are defined by prescription orders. Seven conditions are a composite of diagnosis codes and prescription orders.

Usage

data(xdm20.train)data(xdm20.train)

References

Examples

data(xdm20.train)
head(xdm20.train[ , 1:10])
data(xdm20.train)
head(xdm20.train[ , 1:10])

A data set used in example of `recur.bart`.

Description

Two vectors containing the training and testing set outcomes for a 20% random sample for a real data example of recurrent events survival analysis. There are 100 patients in the cohort: 50 in the training set and 50 in the testing set. See the Reference below (and the References therein) for more detailed information; a brief synopsis follows.

ydm20.train contains the training set only. ydm20.test is provided for completeness; it contains both the training set and the testing set. There are 798 unique time points so there are 50*798=39900 rows of the training set followed by 50*798=39900 rows of the testing set.

The full data sets, ydm.train and ydm.test, can be obtained online at https://www.mcw.edu/-/media/MCW/Departments/Biostatistics/tr064zip.zip There are 488 patients in the full cohort: 235 in the training set and 253 in the testing set.

ydm.train contains the training set only. ydm.test contains both the training set and the testing set. There are 798 unique time points so there are 235*798=187530 rows of the training set followed by 253*798=201894 rows of the testing set.

Usage

data(ydm20.train)
data(ydm20.test)
data(ydm20.train)
data(ydm20.test)

References

Examples

data(ydm20.train)
data(ydm20.test)
table(ydm20.train)
table(ydm20.test)
data(ydm20.train)
data(ydm20.test)
table(ydm20.train)
table(ydm20.test)

Package 'BART'

Help Index

Bayesian Additive Regression Trees

Description

References

AFT BART for time-to-event outcomes

Description

Usage

Arguments

Details

Value

See Also

Examples

AIDS Clinical Trials Group Study 175

Description

Usage

Format

Details

References

American alligator Food Choice

Description

Usage

Format

Details

Source

References

Examples

NHANES 2009-2010 Arthritis Questionnaire

Description

Usage

Details

References

Create a matrix out of a vector or data.frame

Description

Usage

Arguments

See Also

Examples

Bladder Cancer Recurrences

Description

Format

References

Examples

Generates Class Indicator Matrix from a Factor

Description

Usage

Arguments

Value

References

Examples

BART for competing risks

Description

Usage

Arguments

Value

See Also

Examples

Data construction for competing risks with BART

Description

Usage

Arguments

Value

See Also

Examples

BART for competing risks

Description

Usage

Arguments

Value

See Also

Examples

Testing truncated Normal sampling

Description

Usage

Arguments

Value

See Also

Examples

Generalized BART for continuous and binary outcomes

Description