Package 'nftbart' reference manual

Title:	Nonparametric Failure Time Bayesian Additive Regression Trees
Description:	Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Time-to-event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees (HBART) and Low Information Omnibus (LIO) Dirichlet Process Mixtures (DPM). An NFT BART model is of the form Y = mu + f(x) + sd(x) E where functions f and sd have BART and HBART priors, respectively, while E is a nonparametric error distribution due to a DPM LIO prior hierarchy. See the following for a complete description of the model at <doi:10.1111/biom.13857>.
Authors:	Rodney Sparapani [aut, cre], Robert McCulloch [aut], Matthew Pratola [ctb], Hugh Chipman [ctb]
Maintainer:	Rodney Sparapani <[email protected]>
License:	GPL (>= 2)
Version:	2.1
Built:	2024-11-22 06:40:44 UTC
Source:	CRAN

Deprecated: use bMM instead

Description

Create a matrix out of a vector or data.frame. The compiled functions of this package 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, xicuts=NULL, rm.vars=NULL)
bartModelMatrix(X, numcut=0L, usequants=FALSE, type=7, rm.const=FALSE,
                cont=FALSE, xicuts=NULL, rm.vars=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.
`usequants`	If `usequants` is `FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, 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.
`xicuts`	To specify your own cut-points, use the `xicuts` argument.
`rm.vars`	The variables that you want removed.

Value

If numcut==0 (the default), then a matrix of the covariates is returned; otherwise, a list is returned with the following values.

`X`	A matrix of the covariates with `n` rows and `p` columns.
`numcut`	A vector of length `p` of the number of cut-points for each covariate.
`grp`	A vector that corresponds to variables in the input `data.frame` that were translated into dummy columns in the output matrix, i.e., for each input variable in order, there is a number in the vector corresponding to the number of output columns created for it.

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 <- bartModelMatrix(df, numcut=9))

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

## Not run: 
    ## this is an error
    ## 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 <- bartModelMatrix(df, numcut=9))

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

## Not run: 
    ## this is an error
    ## f <- bartModelMatrix(as.character(a))

## End(Not run)

Create a matrix out of a vector or data.frame

Description

Adapted from bartModelMatrix(). The compiled functions of this package 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

bMM(X, numcut=0L, usequants=FALSE, type=7, xicuts=NULL, rm.const=FALSE,
    rm.dupe=FALSE, method="spearman", use="pairwise.complete.obs")
bMM(X, numcut=0L, usequants=FALSE, type=7, xicuts=NULL, rm.const=FALSE,
    rm.dupe=FALSE, method="spearman", use="pairwise.complete.obs")

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.
`usequants`	If `usequants` is `FALSE`, then the cutpoints in `xinfo` are generated uniformly; otherwise, if `TRUE`, quantiles are used for the cutpoints.
`type`	Determines which quantile algorithm is employed.
`xicuts`	To specify your own cut-points, use the `xicuts` argument.
`rm.const`	To remove constant variables or not.
`rm.dupe`	To remove duplicate variables or not.
`method`, `use`	Correlation options.

Value

If numcut==0 (the default), then a matrix of the covariates is returned; otherwise, a list is returned with the following values.

`X`	A matrix of the covariates with `n` rows and `p` columns.
`numcut`	A vector of length `p` of the number of cut-points for each covariate.
`grp`	A vector that corresponds to variables in the input `data.frame` that were translated into dummy columns in the output matrix, i.e., for each input variable in order, there is a number in the vector corresponding to the number of output columns created for it.
`dummy`	Corresponds to `grp` with a two row matrix including the start column of each dummy group in row 1 and the end column in row 2.

Examples


set.seed(99)

a <- rbinom(10, 4, 0.4)

table(a)

x <- runif(10)

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

(b <- bMM(df))

(b <- bMM(df, numcut=9))

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

## Not run: 
    ## this is an error
    f <- bMM(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 <- bMM(df))

(b <- bMM(df, numcut=9))

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

## Not run: 
    ## this is an error
    f <- bMM(as.character(a))

## End(Not run)

NHANES 1999-2000 Body Measures and Demographics

Description

This data set was created from the National Health and Nutrition Examination Survey (NHANES) 1999-2000 Body Measures Exam and Demographics. To create growth charts, this data is restricted to 3435 children aged 2 to 17.

Usage

data(bmx)
data(bmx)

Format

SEQN:	Sequence number
BMXHT:	Height in cm
RIAGENDR:	Gender: 1=male, 2=female
RIDAGEEX:	Age in years with fractions for months
RIDRETH2:	Race/ethnicity: 1=Non-Hispanic White, 2=Non-Hispanic Black, 3=Hispanic
BMXWT:	Weight in kg

References

National Health and Nutrition Examination Survey 1999-2000 Body Measures Exam. https://wwwn.cdc.gov/Nchs/Nhanes/1999-2000/BMX.htm

National Health and Nutrition Examination Survey 1999-2000 Demographics. https://wwwn.cdc.gov/Nchs/Nhanes/1999-2000/DEMO.htm

CDC height for age growth charts

Description

Using the Cole and Green LMS method, here we provide percentiles of height by age and sex based on the US National Center for Health Statistics data for children aged 2 to 17.

Usage

data(CDCheight)
data(CDCheight)

Format

age:	Age in years
sex:	1=male, 2=female
height.XXX:	Height XXXth percentile in cm

References

Cole, Timothy J and Green, Pamela J (1992) Smoothing reference centile curves: the LMS method and penalized likelihood. Statistics in medicine, 11, 1305–1319.

The US Centers for Disease Control and Prevention stature by age LMS parameters https://www.cdc.gov/growthcharts/data/zscore/statage.csv

Cold-deck missing imputation

Description

This function imputes missing data.

Usage

CDimpute(x.train, x.test=matrix(0, 0, 0), impute.bin=NULL)
CDimpute(x.train, x.test=matrix(0, 0, 0), impute.bin=NULL)

Arguments

`x.train`	The training matrix.
`x.test`	The testing matrix, if given.
`impute.bin`	An index of the columns to avoid imputing which will be handled by BART internally.

Details

We call this method cold-decking in analogy to hot-decking. Hot-decking was a method commonly employed with US Census data in the early computing era. For a particular respondent, missing data was imputed by randomly selecting from the responses of their neighbors since it is assumed that the values are likely similar. In our case, we make no assumptions about which values may, or may not, be nearby. We simply take a random sample from the matrix rows to impute the missing data. If the training and testing matrices are the same, then they receive the same imputation.

Value

`x.train`	The imputed training matrix.
`x.test`	The imputed testing matrix.
`miss.train`	A summary of the missing variables for training.
`miss.test`	A summary of the missing variables for testing.
`impute.flag`	Whether `impute.bin` columns were, or were not, imputed.
`same`	Whether `x.train` and `x.test` are identical.

Calculate the C-index/concordance for survival analysis.

Description

The C-index for survival analysis is the corollary of the c statistic (the area under the Receiver Operating Characteristic curve) for binary outcomes. As a probability, the higher is the C-index, the better is the model discrimination vs. lesser probability values. Similarly, the concordance is calculated like the C-index from z-draws via the posterior predictive distribution restricted to the horizon of the data (a la restricted mean survival time).

Usage

Cindex(risk, times, delta=NULL)

concordance(draws, times, delta=NULL)
Cindex(risk, times, delta=NULL)

concordance(draws, times, delta=NULL)

Arguments

`risk`	A vector or prognostic risk scores.
`draws`	A vector of draws via the posterior predictive distribution restricted to the horizon of the data (a la restricted mean survival time).
`times`	A vector of failure times.
`delta`	The corresponding failure time status code: 0, right-censored; 1, failure; or 2, left-censored. Defaults to all failures if not specified.

Value

The return value is the calculated C-index/concordance.

References

Harrell FE, Califf RM, Pryor DB, Lee KL, Rosati RA. (1982) Evaluating the yield of medical tests. JAMA, May 14;247(18):2543-6.

Examples


data(lung)
N=length(lung$status)

##lung$status: 1=censored, 2=dead
##delta: 0=censored, 1=dead
delta=lung$status-1

## this study reports time in days
times=lung$time
times=times/7  ## weeks

## matrix of covariates
x.train=cbind(lung[ , -(1:3)])
## lung$sex:        Male=1 Female=2

## Not run: 
    set.seed(99)
    post=nft(x.train, times, delta, K=0)
    pred=predict(post, x.train, XPtr=TRUE, seed=21)
    print(Cindex(pred$logt.test.mean, times, delta))

## End(Not run)

data(lung)
N=length(lung$status)

##lung$status: 1=censored, 2=dead
##delta: 0=censored, 1=dead
delta=lung$status-1

## this study reports time in days
times=lung$time
times=times/7  ## weeks

## matrix of covariates
x.train=cbind(lung[ , -(1:3)])
## lung$sex:        Male=1 Female=2

## Not run: 
    set.seed(99)
    post=nft(x.train, times, delta, K=0)
    pred=predict(post, x.train, XPtr=TRUE, seed=21)
    print(Cindex(pred$logt.test.mean, times, delta))

## End(Not run)

NCCTG Lung Cancer Data

Description

Survival for 228 patients with advanced lung cancer was recorded up to a median of roughly one year by 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)

Fit NFT BART models.

Description

The nft2()/nft() function is for fitting NFT BART (Nonparametric Failure Time Bayesian Additive Regression Tree) models with different train/test matrices for $f$ and $sd$ functions.

Usage

nft2(
    ## data
    xftrain, xstrain, times, delta=NULL,
    xftest=matrix(nrow=0, ncol=0),
    xstest=matrix(nrow=0, ncol=0),
    rm.const=TRUE, rm.dupe=TRUE,
    ## multi-threading
    tc=getOption("mc.cores", 1), 
    ##MCMC
    nskip=1000, ndpost=2000, nadapt=1000, adaptevery=100,
    chvf=NULL, chvs=NULL,
    method="spearman", use="pairwise.complete.obs",
    pbd=c(0.7, 0.7), pb=c(0.5, 0.5),
    stepwpert=c(0.1, 0.1), probchv=c(0.1, 0.1),
    minnumbot=c(5, 5),
    ## BART and HBART prior parameters
    ntree=c(50, 10), numcut=100, 
    xifcuts=NULL, xiscuts=NULL,
    power=c(2, 2), base=c(0.95, 0.95),
    ## f function
    fmu=NA, k=5, tau=NA, dist='weibull', 
    ## s function
    total.lambda=NA, total.nu=10, mask=NULL,
    ## survival analysis 
    K=100, events=NULL, TSVS=FALSE,
    ## DPM LIO
    drawDPM=1L, 
    alpha=1, alpha.a=1, alpha.b=0.1, alpha.draw=1,
    neal.m=2, constrain=1, 
    m0=0, k0.a=1.5, k0.b=7.5, k0=1, k0.draw=1,
    a0=3, b0.a=2, b0.b=1, b0=1, b0.draw=1,
    ## misc
    na.rm=FALSE, probs=c(0.025, 0.975), printevery=100,
    transposed=FALSE, pred=FALSE
)

nft(
    ## data
    x.train, times, delta=NULL, x.test=matrix(nrow=0, ncol=0),
    rm.const=TRUE, rm.dupe=TRUE,
    ## multi-threading
    tc=getOption("mc.cores", 1), 
    ##MCMC
    nskip=1000, ndpost=2000, nadapt=1000, adaptevery=100,
    chv=NULL,
    method="spearman", use="pairwise.complete.obs",
    pbd=c(0.7, 0.7), pb=c(0.5, 0.5),
    stepwpert=c(0.1, 0.1), probchv=c(0.1, 0.1),
    minnumbot=c(5, 5),
    ## BART and HBART prior parameters
    ntree=c(50, 10), numcut=100, xicuts=NULL,
    power=c(2, 2), base=c(0.95, 0.95),
    ## f function
    fmu=NA, k=5, tau=NA, dist='weibull', 
    ## s function
    total.lambda=NA, total.nu=10, mask=NULL,
    ## survival analysis 
    K=100, events=NULL, TSVS=FALSE,
    ## DPM LIO
    drawDPM=1L, 
    alpha=1, alpha.a=1, alpha.b=0.1, alpha.draw=1,
    neal.m=2, constrain=1, 
    m0=0, k0.a=1.5, k0.b=7.5, k0=1, k0.draw=1,
    a0=3, b0.a=2, b0.b=1, b0=1, b0.draw=1,
    ## misc
    na.rm=FALSE, probs=c(0.025, 0.975), printevery=100,
    transposed=FALSE, pred=FALSE
)

nft2(
    ## data
    xftrain, xstrain, times, delta=NULL,
    xftest=matrix(nrow=0, ncol=0),
    xstest=matrix(nrow=0, ncol=0),
    rm.const=TRUE, rm.dupe=TRUE,
    ## multi-threading
    tc=getOption("mc.cores", 1), 
    ##MCMC
    nskip=1000, ndpost=2000, nadapt=1000, adaptevery=100,
    chvf=NULL, chvs=NULL,
    method="spearman", use="pairwise.complete.obs",
    pbd=c(0.7, 0.7), pb=c(0.5, 0.5),
    stepwpert=c(0.1, 0.1), probchv=c(0.1, 0.1),
    minnumbot=c(5, 5),
    ## BART and HBART prior parameters
    ntree=c(50, 10), numcut=100, 
    xifcuts=NULL, xiscuts=NULL,
    power=c(2, 2), base=c(0.95, 0.95),
    ## f function
    fmu=NA, k=5, tau=NA, dist='weibull', 
    ## s function
    total.lambda=NA, total.nu=10, mask=NULL,
    ## survival analysis 
    K=100, events=NULL, TSVS=FALSE,
    ## DPM LIO
    drawDPM=1L, 
    alpha=1, alpha.a=1, alpha.b=0.1, alpha.draw=1,
    neal.m=2, constrain=1, 
    m0=0, k0.a=1.5, k0.b=7.5, k0=1, k0.draw=1,
    a0=3, b0.a=2, b0.b=1, b0=1, b0.draw=1,
    ## misc
    na.rm=FALSE, probs=c(0.025, 0.975), printevery=100,
    transposed=FALSE, pred=FALSE
)

nft(
    ## data
    x.train, times, delta=NULL, x.test=matrix(nrow=0, ncol=0),
    rm.const=TRUE, rm.dupe=TRUE,
    ## multi-threading
    tc=getOption("mc.cores", 1), 
    ##MCMC
    nskip=1000, ndpost=2000, nadapt=1000, adaptevery=100,
    chv=NULL,
    method="spearman", use="pairwise.complete.obs",
    pbd=c(0.7, 0.7), pb=c(0.5, 0.5),
    stepwpert=c(0.1, 0.1), probchv=c(0.1, 0.1),
    minnumbot=c(5, 5),
    ## BART and HBART prior parameters
    ntree=c(50, 10), numcut=100, xicuts=NULL,
    power=c(2, 2), base=c(0.95, 0.95),
    ## f function
    fmu=NA, k=5, tau=NA, dist='weibull', 
    ## s function
    total.lambda=NA, total.nu=10, mask=NULL,
    ## survival analysis 
    K=100, events=NULL, TSVS=FALSE,
    ## DPM LIO
    drawDPM=1L, 
    alpha=1, alpha.a=1, alpha.b=0.1, alpha.draw=1,
    neal.m=2, constrain=1, 
    m0=0, k0.a=1.5, k0.b=7.5, k0=1, k0.draw=1,
    a0=3, b0.a=2, b0.b=1, b0=1, b0.draw=1,
    ## misc
    na.rm=FALSE, probs=c(0.025, 0.975), printevery=100,
    transposed=FALSE, pred=FALSE
)

Arguments

`xftrain`	n x pf matrix of predictor variables for the training data.
`xstrain`	n x ps matrix of predictor variables for the training data.
`x.train`	n x p matrix of predictor variables for the training data.
`times`	n x 1 vector of the observed times for the training data.
`delta`	n x 1 vector of the time type for the training data: 0, for right-censoring; 1, for an event; and, 2, for left-censoring.
`xftest`	m x pf matrix of predictor variables for the test set.
`xstest`	m x ps matrix of predictor variables for the test set.
`x.test`	m x p matrix of predictor variables for the test set.
`rm.const`	To remove constant variables or not.
`rm.dupe`	To remove duplicate variables or not.
`tc`	Number of OpenMP threads to use.
`nskip`	Number of MCMC iterations to burn-in and discard.
`ndpost`	Number of MCMC iterations kept after burn-in.
`nadapt`	Number of MCMC iterations for adaptation prior to burn-in.
`adaptevery`	Adapt MCMC proposal distributions every `adaptevery` iteration.
`chvf`, `chvs`, `chv`	Predictor correlation matrix used as a pre-conditioner for MCMC change-of-variable proposals.
`method`, `use`	Correlation options for change-of-variable proposal pre-conditioner.
`pbd`	Probability of performing a birth/death proposal, otherwise perform a rotate proposal.
`pb`	Probability of performing a birth proposal given that we choose to perform a birth/death proposal.
`stepwpert`	Initial width of proposal distribution for peturbing cut-points.
`probchv`	Probability of performing a change-of-variable proposal. Otherwise, only do a perturb proposal.
`minnumbot`	Minimum number of observations required in leaf (terminal) nodes.
`ntree`	Vector of length two for the number of trees used for the mean model and the number of trees used for the variance model.
`numcut`	Number of cutpoints to use for each predictor variable.
`xifcuts`, `xiscuts`, `xicuts`	More detailed construction of cut-points can be specified by the `xicuts` function and provided here.
`power`	Power parameter in the tree depth penalizing prior.
`base`	Base parameter in the tree depth penalizing prior.
`fmu`	Prior parameter for the center of the mean model.
`k`	Prior parameter for the mean model.
`tau`	Desired `SD/ntree` for f function leaf prior if known.
`dist`	Distribution to be passed to intercept-only AFT model to center `y.train`.
`total.lambda`	A rudimentary estimate of the process standard deviation. Used in calibrating the variance prior.
`total.nu`	Shape parameter for the variance prior.
`mask`	If a proportion is provided, then said quantile of `max.i sd(x.i)` is used to mask non-stationary departures (with respect to convergence) above this threshold.
`K`	Number of grid points for which to estimate survival probability.
`events`	Grid points for which to estimate survival probability.
`TSVS`	Setting to `TRUE` will avoid unnecessary processing for Thompson sampling variable selection, i.e., all that is needed is the variable counts from the tree branch decision rules.
`drawDPM`	Whether to utilize DPM or not.
`alpha`	Initial value of DPM concentration parameter.
`alpha.a`	Gamma prior parameter setting for DPM concentration parameter where E[`alpha`]=`alpha.a`/`alpha.b`.
`alpha.b`	See `alpha.a` above.
`alpha.draw`	Whether to draw `alpha` or it is fixed at the initial value.
`neal.m`	The number of additional atoms for Neal 2000 DPM algorithm 8.
`constrain`	Whether to perform constained DPM or unconstrained.
`m0`	Center of the error distribution: defaults to zero.
`k0.a`	First Gamma prior argument for `k0`.
`k0.b`	Second Gamma prior argument for `k0`.
`k0`	Initial value of `k0`.
`k0.draw`	Whether to fix k0 or draw it if from the DPM LIO prior hierarchy: `k0~Gamma(k0.a, k0.b)`, i.e., `E[k0]=k0.a/k0.b`.
`a0`	First Gamma prior argument for $tau$ .
`b0.a`	First Gamma prior argument for `b0`.
`b0.b`	Second Gamma prior argument for `b0`.
`b0`	Initial value of `b0`.
`b0.draw`	Whether to fix b0 or draw it from the DPM LIO prior hierarchy: `b0~Gamma(b0.a, b0.b)`, i.e., `E[b0]=b0.a/b0.b`.
`na.rm`	Value to be passed to the `predict` function.
`probs`	Value to be passed to the `predict` function.
`printevery`	Outputs MCMC algorithm status every printevery iterations.
`transposed`	Specify `TRUE` if all of the pre-processing for `xftrain/xstrain/xftest/xstest` has been conducted prior to the call (including tranposing).
`pred`	Specify `TRUE` if you want to return the `pred` item that is used to calculate `soffset`.

Details

nft2()/nft() is the function to fit time-to-event data. The most general form of the model allowed is $Y({\bf x})=mu+f({\bf x})+sd({\bf x})Z$ where $E$ follows a nonparametric error distribution by default. The nft2()/nft() function returns a fit object of S3 class type nft2/nft that is essentially a list containing the following items.

Value

`ots`, `oid`, `ovar`, `oc`, `otheta`	These are `XPtr`s to the BART $f(x)$ objects in RAM that are only available for fits generated in the current R session.
`sts`, `sid`, `svar`, `sc`, `stheta`	Similarly, these are `XPtr`s to the HBART $sd(x)$ objects.
`fmu`	The constant $mu$ .
`f.train`, `s.train`	The trained $f(x)$ and $sd(x)$ respectively: matrices with `ndpost` rows and $n$ columns.
`f.train.mean`, `s.train.mean`	The posterior mean of the trained $f(x)$ and $sd(x)$ respectively: vectors of length $n$ .
`f.trees`, `s.trees`	Character strings representing the trained fits of $f(x)$ and $sd(x)$ respectively to facilitate usage of the `predict` function when `XPtr`s are unavailable.
`dpalpha`	The draws of the DPM concentration parameter $alpha$ .
`dpn`, `dpn.`	The number of atom clusters per DPM, $J$ , for all draws including burn-in and excluding burn-in respectively.
`dpmu`	The draws of the DPM parameter $mu[i]$ where $i=1,...,n$ indexes subjects: a matrix with `ndpost` rows and $n$ columns.
`dpmu.`	The draws of the DPM parameter $mu[j]$ where $j=1,...,J$ indexes atom clusters: a matrix with `ndpost` rows and $J$ columns.
`dpwt.`	The weights for efficient DPM calculations by atom clusters (as opposed to subjects) for use with $dpmu.$ (and $dpsd.$ ; see below): a matrix with `ndpost` rows and $J$ columns.
`dpsd`, `dpsd.`	Similarly, the draws of the DPM parameter $tau[i]$ transformed into the standard deviation $sigma[i]$ for convenience.
`dpC`	The indices $j$ for each subject $i$ corresponding to their shared atom cluster.
`z.train`	The data values/augmentation draws of $log t$ .
`f.tmind/f.tavgd/f.tmaxd`	The min/average/max tier degree of trees in the $f$ ensemble.
`s.tmind/s.tavgd/s.tmaxd`	The min/average/max tier degree of trees in the $s$ ensemble.
`f.varcount`, `s.varcount`	Variable importance counts of branch decision rules for each $x$ of $f$ and $s$ respectively: matrices with `ndpost` rows and $p$ columns.
`f.varcount.mean`, `s.varcount.mean`	Similarly, the posterior mean of the variable importance counts for each $x$ of $f$ and $s$ respectively: vectors of length $p$ .
`f.varprob`, `s.varprob`	Similarly, re-weighting the posterior mean of the variable importance counts as sum-to-one probabilities for each $x$ of $f$ and $s$ respectively: vectors of length $p$ .
`LPML`	The log Pseudo-Marginal Likelihood as typically calculated for right-/left-censoring.
`pred`	The object returned from the `predict` function where `x.test=x.train` in order to calculate the `soffset` item that is needed to use `predict` when `XPtr`s are not available.
`soffset`	See `pred` above.
`aft`	The AFT model fit used to initialize NFT BART.
`elapsed`	The elapsed time of the run in seconds.

Author(s)

Rodney Sparapani: [email protected]

References

Sparapani R., Logan B., Maiers M., Laud P., McCulloch R. (2023) Nonparametric Failure Time: Time-to-event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees and Low Information Omnibus Dirichlet Process Mixtures Biometrics (ahead of print) <doi:10.1111/biom.13857>.

Examples


##library(nftbart)
data(lung)
N=length(lung$status)

##lung$status: 1=censored, 2=dead
##delta: 0=censored, 1=dead
delta=lung$status-1

## this study reports time in days rather than weeks or months
times=lung$time
times=times/7  ## weeks

## matrix of covariates
x.train=cbind(lung[ , -(1:3)])
## lung$sex:        Male=1 Female=2

## token run just to test installation
post=nft2(x.train, x.train, times, delta, K=0,
         nskip=0, ndpost=10, nadapt=4, adaptevery=1)


set.seed(99)
post=nft2(x.train, x.train, times, delta, K=0)
XPtr=TRUE

x.test = rbind(x.train, x.train)
x.test[ , 2]=rep(1:2, each=N)
K=75
events=seq(0, 150, length.out=K+1)
pred = predict(post, x.test, x.test, K=K, events=events[-1],
               XPtr=XPtr, FPD=TRUE)

plot(events, c(1, pred$surv.fpd.mean[1:K]), type='l', col=4,
     ylim=0:1, 
     xlab=expression(italic(t)), sub='weeks',
     ylab=expression(italic(S)(italic(t), italic(x))))
lines(events, c(1, pred$surv.fpd.upper[1:K]), lty=2, lwd=2, col=4)
lines(events, c(1, pred$surv.fpd.lower[1:K]), lty=2, lwd=2, col=4)
lines(events, c(1, pred$surv.fpd.mean[K+1:K]), lwd=2, col=2)
lines(events, c(1, pred$surv.fpd.upper[K+1:K]), lty=2, lwd=2, col=2)
lines(events, c(1, pred$surv.fpd.lower[K+1:K]), lty=2, lwd=2, col=2)
legend('topright', c('Adv. lung cancer\nmortality example',
                     'M', 'F'), lwd=2, col=c(0, 4, 2), lty=1)


##library(nftbart)
data(lung)
N=length(lung$status)

##lung$status: 1=censored, 2=dead
##delta: 0=censored, 1=dead
delta=lung$status-1

## this study reports time in days rather than weeks or months
times=lung$time
times=times/7  ## weeks

## matrix of covariates
x.train=cbind(lung[ , -(1:3)])
## lung$sex:        Male=1 Female=2

## token run just to test installation
post=nft2(x.train, x.train, times, delta, K=0,
         nskip=0, ndpost=10, nadapt=4, adaptevery=1)


set.seed(99)
post=nft2(x.train, x.train, times, delta, K=0)
XPtr=TRUE

x.test = rbind(x.train, x.train)
x.test[ , 2]=rep(1:2, each=N)
K=75
events=seq(0, 150, length.out=K+1)
pred = predict(post, x.test, x.test, K=K, events=events[-1],
               XPtr=XPtr, FPD=TRUE)

plot(events, c(1, pred$surv.fpd.mean[1:K]), type='l', col=4,
     ylim=0:1, 
     xlab=expression(italic(t)), sub='weeks',
     ylab=expression(italic(S)(italic(t), italic(x))))
lines(events, c(1, pred$surv.fpd.upper[1:K]), lty=2, lwd=2, col=4)
lines(events, c(1, pred$surv.fpd.lower[1:K]), lty=2, lwd=2, col=4)
lines(events, c(1, pred$surv.fpd.mean[K+1:K]), lwd=2, col=2)
lines(events, c(1, pred$surv.fpd.upper[K+1:K]), lty=2, lwd=2, col=2)
lines(events, c(1, pred$surv.fpd.lower[K+1:K]), lty=2, lwd=2, col=2)
legend('topright', c('Adv. lung cancer\nmortality example',
                     'M', 'F'), lwd=2, col=c(0, 4, 2), lty=1)

Estimating the survival and the hazard for AFT BART models.

Description

The function predict.aftree() is provided for performing posterior inference via test data set estimates stored in a aftree object returned from AFTree() in a similar fashion as that of predict.nft. N.B. the x.test matrix must be provided on the AFTree() function call. Here we are only calculating the survival function by default, and, if requested, the hazard as well.

Usage

## S3 method for class 'aftree'
predict(
            ## data
            object,
            ## predictions
            events=NULL,
            FPD=FALSE,
            probs=c(0.025, 0.975),
            take.logs=TRUE,
            seed=NULL,
            ## default settings
            ndpost=nrow(object$mix.prop),
            nclust=ncol(object$mix.prop),
            ## etc.
            ...)
## S3 method for class 'aftree'
predict(
            ## data
            object,
            ## predictions
            events=NULL,
            FPD=FALSE,
            probs=c(0.025, 0.975),
            take.logs=TRUE,
            seed=NULL,
            ## default settings
            ndpost=nrow(object$mix.prop),
            nclust=ncol(object$mix.prop),
            ## etc.
            ...)

Arguments

`object`	Object of type `nft` from a previous call to `nft()`.
`events`	You must specify a grid of time-points; however, they can be a matrix with rows for each subject.
`FPD`	Whether to yield the usual predictions or marginal predictions calculated by the partial dependence function.
`probs`	A vector of length two containing the lower and upper quantiles to be calculated for the predictions.
`take.logs`	Whether or not to take logarithms.
`seed`	If provided, then this value is used to generate random natural logarithms of event times from the predictive distribution.
`ndpost`	The number of MCMC samples generated.
`nclust`	The number of DPM clusters generated.
`...`	The et cetera objects passed to the `predict` method. Currently, it has no functionality.

Details

Returns a list with the following entries. If hazard=TRUE is specified, then a similar set of entries for the hazard are produced.

Value

`surv.fpd`	Survival function posterior draws on a grid of time-points by the partial dependence function when requested.
`surv.fpd.mean`	Survival function estimates on a grid of time-points by the partial dependence function when requested.
`surv.fpd.lower`	Survival function lower quantiles on a grid of time-points by the partial dependence function when requested.
`surv.fpd.upper`	Survival function upper quantiles on a grid of time-points by the partial dependence function when requested.

Author(s)

Rodney Sparapani: [email protected]

Drawing Posterior Predictive Realizations for NFT BART models.

Description

The function predict.nft2()/predict.nft() is the main function for drawing posterior predictive realizations at new inputs using a fitted model stored in a nft2/nft object returned from nft2()/nft().

Usage

## S3 method for class 'nft2'
predict(
            ## data
            object,
            xftest=object$xftrain,
            xstest=object$xstrain,
            ## multi-threading
            tc=getOption("mc.cores", 1), ##OpenMP thread count
            ## current process fit vs. previous process fit
            XPtr=TRUE,
            ## predictions
            K=0,
            events=object$events,
            FPD=FALSE,
            probs=c(0.025, 0.975),
            take.logs=TRUE,
            na.rm=FALSE,
            RMST.max=NULL,
            ## default settings for NFT:BART/HBART/DPM
            fmu=object$NFT$fmu,
            soffset=object$soffset,
            drawDPM=object$drawDPM,
            ## etc.
            ...)

## S3 method for class 'nft'
predict(
            ## data
            object,
            x.test=object$x.train,
            ## multi-threading
            tc=getOption("mc.cores", 1), ##OpenMP thread count
            ## current process fit vs. previous process fit
            XPtr=TRUE,
            ## predictions
            K=0,
            events=object$events,
            FPD=FALSE,
            probs=c(0.025, 0.975),
            take.logs=TRUE,
            na.rm=FALSE,
            RMST.max=NULL,
            ## default settings for NFT:BART/HBART/DPM
            fmu=object$NFT$fmu,
            soffset=object$soffset,
            drawDPM=object$drawDPM,
            ## etc.
            ...)
## S3 method for class 'nft2'
predict(
            ## data
            object,
            xftest=object$xftrain,
            xstest=object$xstrain,
            ## multi-threading
            tc=getOption("mc.cores", 1), ##OpenMP thread count
            ## current process fit vs. previous process fit
            XPtr=TRUE,
            ## predictions
            K=0,
            events=object$events,
            FPD=FALSE,
            probs=c(0.025, 0.975),
            take.logs=TRUE,
            na.rm=FALSE,
            RMST.max=NULL,
            ## default settings for NFT:BART/HBART/DPM
            fmu=object$NFT$fmu,
            soffset=object$soffset,
            drawDPM=object$drawDPM,
            ## etc.
            ...)

## S3 method for class 'nft'
predict(
            ## data
            object,
            x.test=object$x.train,
            ## multi-threading
            tc=getOption("mc.cores", 1), ##OpenMP thread count
            ## current process fit vs. previous process fit
            XPtr=TRUE,
            ## predictions
            K=0,
            events=object$events,
            FPD=FALSE,
            probs=c(0.025, 0.975),
            take.logs=TRUE,
            na.rm=FALSE,
            RMST.max=NULL,
            ## default settings for NFT:BART/HBART/DPM
            fmu=object$NFT$fmu,
            soffset=object$soffset,
            drawDPM=object$drawDPM,
            ## etc.
            ...)

Arguments

`object`	Object of type `nft2/nft` from a previous call to `nft2()/nft()`.
`xftest`, `xstest`, `x.test`	New input settings in the form of a matrix at which to construct predictions. Defaults to the training inputs.
`tc`	Number of OpenMP threads to use for parallel computing.
`XPtr`	If `object` was created during the currently running R process, then (via an Rcpp `XPtr`) the BART/HBART tree ensemble objects can be accessed in RAM; otherwise, those objects will need to be loaded from their string encodings.
`K`	The length of the grid of time-points to be used for survival predictions. Set to zero to avoid these calculations which can be time-consuming for large data sets.
`events`	You can specify the grid of time-points; otherwise, they are derived from quantiles of the augmented event times.
`FPD`	Whether to yield the usual predictions or marginal predictions calculated by the partial dependence function.
`probs`	A vector of length two containing the lower and upper quantiles to be calculated for the predictions.
`take.logs`	Whether or not to take logarithms.
`na.rm`	Whether `NA` values should be removed from the summaries.
`RMST.max`	To calculate Restricted Mean Survival Time (RMST), we need to set a reasonable time maxima. Typically, a clinically important time that a majority (or a large plurality) of censored subjects have been followed through that point or beyond.
`fmu`	BART centering parameter for the test data. Defaults to the value used by `nft2()/nft()` when training the model.
`soffset`	HBART centering parameter for the test data. Defaults to the value used by `nft2()/nft()` when training the model.
`drawDPM`	Whether NFT BART was fit with, or without, DPM.
`...`	The et cetera objects passed to the `predict` method. Currently, it has no functionality.

Details

predict.nft2()/predict.nft() is the main function for calculating posterior predictions and uncertainties once a model has been fit by nft2()/nft().

Returns a list with the following entries.

Value

`f.test`	Posterior realizations of the mean function stored in a matrix. Omitted if partial dependence functions are performed since these will typically be large.
`s.test`	Posterior realizations of the SD function stored in a matrix. Omitted if partial dependence functions are performed since these will typically be large.
`f.test.mean`	Posterior predictive mean of mean function.
`f.test.lower`	Posterior predictive lower quantile of mean function.
`f.test.upper`	Posterior predictive upper quantile of mean function.
`s.test.mean`	Posterior predictive mean of SD function.
`s.test.lower`	Posterior predictive lower quantile of SD function.
`s.test.upper`	Posterior predictive upper quantile of SD function.
`surv.fpd`	Survival function posterior draws on a grid of time-points by the partial dependence function when requested.
`surv.fpd.mean`	Survival function estimates on a grid of time-points by the partial dependence function when requested.
`surv.fpd.lower`	Survival function lower quantiles on a grid of time-points by the partial dependence function when requested.
`surv.fpd.upper`	Survival function upper quantiles on a grid of time-points by the partial dependence function when requested.

Author(s)

Rodney Sparapani: [email protected]

Variable selection with NFT BART models.

Description

The tsvs2()/tsvs() function is for Thompson sampling variable selection with NFT BART.

Usage

tsvs2(
               ## data
               xftrain, xstrain, times, delta=NULL, 
               rm.const=TRUE, rm.dupe=TRUE,
               ##tsvs args
               K=20, a.=1, b.=0.5, C=0.5,
               rds.file='tsvs2.rds', pdf.file='tsvs2.pdf',
               ## multi-threading
               tc=getOption("mc.cores", 1), ##OpenMP thread count
               ##MCMC
               nskip=1000, ndpost=2000, 
               nadapt=1000, adaptevery=100, 
               chvf=NULL, chvs=NULL,
               method="spearman", use="pairwise.complete.obs",
               pbd=c(0.7, 0.7), pb=c(0.5, 0.5),
               stepwpert=c(0.1, 0.1), probchv=c(0.1, 0.1),
               minnumbot=c(5, 5),
               ## BART and HBART prior parameters
               ntree=c(10, 2), numcut=100,
               xifcuts=NULL, xiscuts=NULL,
               power=c(2, 2), base=c(0.95, 0.95),
               ## f function
               fmu=NA, k=5, tau=NA, dist='weibull', 
               ## s function
               total.lambda=NA, total.nu=10, mask=0.95,
               ## survival analysis 
               ##K=100, events=NULL, 
               ## DPM LIO
               drawDPM=1L, 
               alpha=1, alpha.a=1, alpha.b=0.1, alpha.draw=1,
               neal.m=2, constrain=1, 
               m0=0, k0.a=1.5, k0.b=7.5, k0=1, k0.draw=1,
               a0=3, b0.a=2, b0.b=1, b0=1, b0.draw=1,
               ## misc
               na.rm=FALSE, probs=c(0.025, 0.975), printevery=100,
               transposed=FALSE
)

tsvs(
               ## data
               x.train, times, delta=NULL, 
               rm.const=TRUE, rm.dupe=TRUE,
               ##tsvs args
               K=20, a.=1, b.=0.5, C=0.5,
               rds.file='tsvs.rds', pdf.file='tsvs.pdf',
               ## multi-threading
               tc=getOption("mc.cores", 1), ##OpenMP thread count
               ##MCMC
               nskip=1000, ndpost=2000, 
               nadapt=1000, adaptevery=100, 
               chv=NULL,
               method="spearman", use="pairwise.complete.obs",
               pbd=c(0.7, 0.7), pb=c(0.5, 0.5),
               stepwpert=c(0.1, 0.1), probchv=c(0.1, 0.1),
               minnumbot=c(5, 5),
               ## BART and HBART prior parameters
               ntree=c(10, 2), numcut=100, xicuts=NULL,
               power=c(2, 2), base=c(0.95, 0.95),
               ## f function
               fmu=NA, k=5, tau=NA, dist='weibull', 
               ## s function
               total.lambda=NA, total.nu=10, mask=0.95,
               ## survival analysis 
               ##K=100, events=NULL, 
               ## DPM LIO
               drawDPM=1L, 
               alpha=1, alpha.a=1, alpha.b=0.1, alpha.draw=1,
               neal.m=2, constrain=1, 
               m0=0, k0.a=1.5, k0.b=7.5, k0=1, k0.draw=1,
               a0=3, b0.a=2, b0.b=1, b0=1, b0.draw=1,
               ## misc
               na.rm=FALSE, probs=c(0.025, 0.975), printevery=100,
               transposed=FALSE
)

tsvs2(
               ## data
               xftrain, xstrain, times, delta=NULL, 
               rm.const=TRUE, rm.dupe=TRUE,
               ##tsvs args
               K=20, a.=1, b.=0.5, C=0.5,
               rds.file='tsvs2.rds', pdf.file='tsvs2.pdf',
               ## multi-threading
               tc=getOption("mc.cores", 1), ##OpenMP thread count
               ##MCMC
               nskip=1000, ndpost=2000, 
               nadapt=1000, adaptevery=100, 
               chvf=NULL, chvs=NULL,
               method="spearman", use="pairwise.complete.obs",
               pbd=c(0.7, 0.7), pb=c(0.5, 0.5),
               stepwpert=c(0.1, 0.1), probchv=c(0.1, 0.1),
               minnumbot=c(5, 5),
               ## BART and HBART prior parameters
               ntree=c(10, 2), numcut=100,
               xifcuts=NULL, xiscuts=NULL,
               power=c(2, 2), base=c(0.95, 0.95),
               ## f function
               fmu=NA, k=5, tau=NA, dist='weibull', 
               ## s function
               total.lambda=NA, total.nu=10, mask=0.95,
               ## survival analysis 
               ##K=100, events=NULL, 
               ## DPM LIO
               drawDPM=1L, 
               alpha=1, alpha.a=1, alpha.b=0.1, alpha.draw=1,
               neal.m=2, constrain=1, 
               m0=0, k0.a=1.5, k0.b=7.5, k0=1, k0.draw=1,
               a0=3, b0.a=2, b0.b=1, b0=1, b0.draw=1,
               ## misc
               na.rm=FALSE, probs=c(0.025, 0.975), printevery=100,
               transposed=FALSE
)

tsvs(
               ## data
               x.train, times, delta=NULL, 
               rm.const=TRUE, rm.dupe=TRUE,
               ##tsvs args
               K=20, a.=1, b.=0.5, C=0.5,
               rds.file='tsvs.rds', pdf.file='tsvs.pdf',
               ## multi-threading
               tc=getOption("mc.cores", 1), ##OpenMP thread count
               ##MCMC
               nskip=1000, ndpost=2000, 
               nadapt=1000, adaptevery=100, 
               chv=NULL,
               method="spearman", use="pairwise.complete.obs",
               pbd=c(0.7, 0.7), pb=c(0.5, 0.5),
               stepwpert=c(0.1, 0.1), probchv=c(0.1, 0.1),
               minnumbot=c(5, 5),
               ## BART and HBART prior parameters
               ntree=c(10, 2), numcut=100, xicuts=NULL,
               power=c(2, 2), base=c(0.95, 0.95),
               ## f function
               fmu=NA, k=5, tau=NA, dist='weibull', 
               ## s function
               total.lambda=NA, total.nu=10, mask=0.95,
               ## survival analysis 
               ##K=100, events=NULL, 
               ## DPM LIO
               drawDPM=1L, 
               alpha=1, alpha.a=1, alpha.b=0.1, alpha.draw=1,
               neal.m=2, constrain=1, 
               m0=0, k0.a=1.5, k0.b=7.5, k0=1, k0.draw=1,
               a0=3, b0.a=2, b0.b=1, b0=1, b0.draw=1,
               ## misc
               na.rm=FALSE, probs=c(0.025, 0.975), printevery=100,
               transposed=FALSE
)

Arguments

`xftrain`	n x pf matrix of predictor variables for the training data.
`xstrain`	n x ps matrix of predictor variables for the training data.
`x.train`	n x ps matrix of predictor variables for the training data.
`times`	nx1 vector of the observed times for the training data.
`delta`	nx1 vector of the time type for the training data: 0, for right-censoring; 1, for an event; and, 2, for left-censoring.
`rm.const`	To remove constant variables or not.
`rm.dupe`	To remove duplicate variables or not.
`K`	The number of Thompson sampling steps to take. Not to be confused with the size of the time grid for survival distribution estimation.
`a.`	The prior parameter for successes of a Beta distribution.
`b.`	The prior parameter for failures of a Beta distribution.
`C`	The probability cut-off for variable selection.
`rds.file`	File name to store RDS object containing Thompson sampling parameters.
`pdf.file`	File name to store PDF graphic of variables selected.
`tc`	Number of OpenMP threads to use.
`nskip`	Number of MCMC iterations to burn-in and discard.
`ndpost`	Number of MCMC iterations kept after burn-in.
`nadapt`	Number of MCMC iterations for adaptation prior to burn-in.
`adaptevery`	Adapt MCMC proposal distributions every `adaptevery` iteration.
`chvf`, `chvs`, `chv`	Predictor correlation matrix used as a pre-conditioner for MCMC change-of-variable proposals.
`method`, `use`	Correlation options for change-of-variable proposal pre-conditioner.
`pbd`	Probability of performing a birth/death proposal, otherwise perform a rotate proposal.
`pb`	Probability of performing a birth proposal given that we choose to perform a birth/death proposal.
`stepwpert`	Initial width of proposal distribution for peturbing cut-points.
`probchv`	Probability of performing a change-of-variable proposal. Otherwise, only do a perturb proposal.
`minnumbot`	Minimum number of observations required in leaf (terminal) nodes.
`ntree`	Vector of length two for the number of trees used for the mean model and the number of trees used for the variance model.
`numcut`	Number of cutpoints to use for each predictor variable.
`xifcuts`, `xiscuts`, `xicuts`	More detailed construction of cut-points can be specified by the `xicuts` function and provided here.
`power`	Power parameter in the tree depth penalizing prior.
`base`	Base parameter in the tree depth penalizing prior.
`fmu`	Prior parameter for the center of the mean model.
`k`	Prior parameter for the mean model.
`tau`	Desired `SD/ntree` for f function leaf prior if known.
`dist`	Distribution to be passed to intercept-only AFT model to center `y.train`.
`total.lambda`	A rudimentary estimate of the process standard deviation. Used in calibrating the variance prior.
`total.nu`	Shape parameter for the variance prior.
`mask`	If a proportion is provided, then said quantile of `max.i sd(x.i)` is used to mask non-stationary departures (with respect to convergence) above this threshold.
`drawDPM`	Whether to utilize DPM or not.
`alpha`	Initial value of DPM concentration parameter.
`alpha.a`	Gamma prior parameter setting for DPM concentration parameter where E[`alpha`]=`alpha.a`/`alpha.b`.
`alpha.b`	See `alpha.a` above.
`alpha.draw`	Whether to draw `alpha` or it is fixed at the initial value.
`neal.m`	The number of additional atoms for Neal 2000 DPM algorithm 8.
`constrain`	Whether to perform constained DPM or unconstrained.
`m0`	Center of the error distribution: defaults to zero.
`k0.a`	First Gamma prior argument for `k0`.
`k0.b`	Second Gamma prior argument for `k0`.
`k0`	Initial value of `k0`.
`k0.draw`	Whether to fix k0 or draw it if from the DPM LIO prior hierarchy: `k0~Gamma(k0.a, k0.b)`, i.e., `E[k0]=k0.a/k0.b`.
`a0`	First Gamma prior argument for $tau$ .
`b0.a`	First Gamma prior argument for `b0`.
`b0.b`	Second Gamma prior argument for `b0`.
`b0`	Initial value of `b0`.
`b0.draw`	Whether to fix b0 or draw it from the DPM LIO prior hierarchy: `b0~Gamma(b0.a, b0.b)`, i.e., `E[b0]=b0.a/b0.b`.
`na.rm`	Value to be passed to the `predict` function.
`probs`	Value to be passed to the `predict` function.
`printevery`	Outputs MCMC algorithm status every printevery iterations.
`transposed`	`tsvs` handles all of the pre-processing for `x.train/x.test` (including tranposing) computational efficiency.

Details

tsvs2()/tsvs() is the function to perform variable selection. The tsvs2()/tsvs() function returns a fit object of S3 class type list as well as storing it in rds.file for sampling in progress.

Author(s)

Rodney Sparapani: [email protected]

References

Liu Y., Rockova V. (2021) Variable selection via Thompson sampling. Journal of the American Statistical Association. Jun 29:1-8.

Examples


##library(nftbart)
data(lung)
N=length(lung$status)

##lung$status: 1=censored, 2=dead
##delta: 0=censored, 1=dead
delta=lung$status-1

## this study reports time in days rather than weeks or months
times=lung$time
times=times/7  ## weeks

## matrix of covariates
x.train=cbind(lung[ , -(1:3)])
## lung$sex:        Male=1 Female=2


##vars=tsvs2(x.train, x.train, times, delta)
vars=tsvs2(x.train, x.train, times, delta, K=0) ## K=0 just returns 0

##library(nftbart)
data(lung)
N=length(lung$status)

##lung$status: 1=censored, 2=dead
##delta: 0=censored, 1=dead
delta=lung$status-1

## this study reports time in days rather than weeks or months
times=lung$time
times=times/7  ## weeks

## matrix of covariates
x.train=cbind(lung[ , -(1:3)])
## lung$sex:        Male=1 Female=2


##vars=tsvs2(x.train, x.train, times, delta)
vars=tsvs2(x.train, x.train, times, delta, K=0) ## K=0 just returns 0

Specifying cut-points for the covariates

Description

This function allows you to create a list that specifies the cut-points for the covariates.

Usage

xicuts(x.train, transposed=FALSE, numcut=100)
xicuts(x.train, transposed=FALSE, numcut=100)

Arguments

`x.train`	The training matrix to derive cut-points from.
`transposed`	Whether or not the matrix has been tranposed yet.
`numcut`	The number of cut-points to create.

Details

The cut-points are generated uniformly from min. to max., i.e., the distribution of the data is ignored.

Value

An object is returned of type BARTcutinfo which is essentially a list.

Package 'nftbart'

Help Index

Deprecated: use bMM instead

Description

Usage

Arguments

Value

See Also

Examples

Create a matrix out of a vector or data.frame

Description

Usage

Arguments

Value

See Also

Examples

NHANES 1999-2000 Body Measures and Demographics

Description

Usage

Format

References

CDC height for age growth charts

Description

Usage

Format

References

Cold-deck missing imputation

Description

Usage

Arguments

Details

Value

Calculate the C-index/concordance for survival analysis.

Description

Usage

Arguments

Value

References

See Also

Examples

NCCTG Lung Cancer Data

Description

Format

Source

References

Examples

Fit NFT BART models.

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Estimating the survival and the hazard for AFT BART models.

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Drawing Posterior Predictive Realizations for NFT BART models.

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Variable selection with NFT BART models.

Description

Usage

Arguments

Details

Author(s)

References

See Also