Package 'rms'

Description

Subset Method for Ocens Objects

Usage

## S3 method for class 'Ocens'
x[rows = 1:d[1], cols = 1:d[2], ...]

Arguments

Details

Subsets an Ocens object, preserving its special attributes. Attributes are not updated. In the future such updating should be implemented.

Value

new Ocens object

Author(s)

Frank Harrell

Description

The anova function automatically tests most meaningful hypotheses in a design. For example, suppose that age and cholesterol are predictors, and that a general interaction is modeled using a restricted spline surface. anova prints Wald statistics ($F$ statistics for an ols fit) for testing linearity of age, linearity of cholesterol, age effect (age + age by cholesterol interaction), cholesterol effect (cholesterol + age by cholesterol interaction), linearity of the age by cholesterol interaction (i.e., adequacy of the simple age * cholesterol 1 d.f. product), linearity of the interaction in age alone, and linearity of the interaction in cholesterol alone. Joint tests of all interaction terms in the model and all nonlinear terms in the model are also performed. For any multiple d.f. effects for continuous variables that were not modeled through rcs, pol, lsp, etc., tests of linearity will be omitted. This applies to matrix predictors produced by e.g. poly or ns.

For lrm, orm, cph, psm and Glm fits, the better likelihood ratio chi-square tests may be obtained by specifying test='LR'. Fits must use x=TRUE, y=TRUE to run LR tests. The tests are run fairly efficiently by subsetting the design matrix rather than recreating it.

print.anova.rms is the printing method. plot.anova.rms draws dot charts depicting the importance of variables in the model, as measured by Wald or LR $\chi^2$, $\chi^2$ minus d.f., AIC, $P$-values, partial $R^2$, $R^2$ for the whole model after deleting the effects in question, or proportion of overall model $R^2$ that is due to each predictor. latex.anova.rms is the latex method. It substitutes Greek/math symbols in column headings, uses boldface for TOTAL lines, and constructs a caption. Then it passes the result to latex.default for conversion to LaTeX.

When the anova table was converted to account for missing data imputation by processMI, a separate function prmiInfo can be used to print information related to imputation adjustments.

For Bayesian models such as blrm, anova computes relative explained variation indexes (REV) based on approximate Wald statistics. This uses the variance-covariance matrix of all of the posterior draws, and the individual draws of betas, plus an overall summary from the posterior mode/mean/median beta. Wald chi-squares assuming multivariate normality of betas are computed just as with frequentist models, and for each draw (or for the summary) the ratio of the partial Wald chi-square to the total Wald statistic for the model is computed as REV.

The print method calls latex or html methods depending on options(prType=). For latex a table environment is not used and an ordinary tabular is produced. When using html with Quarto or RMarkdown, results='asis' need not be written in the chunk header.

html.anova.rms just calls latex.anova.rms.

Usage

## S3 method for class 'rms'
anova(object, ..., main.effect=FALSE, tol=.Machine$double.eps,
      test=c('F','Chisq','LR'), india=TRUE, indnl=TRUE, ss=TRUE,
      vnames=c('names','labels'),
      posterior.summary=c('mean', 'median', 'mode'), ns=500, cint=0.95,
      fitargs=NULL)

## S3 method for class 'anova.rms'
print(x,
      which=c('none','subscripts','names','dots'),
      table.env=FALSE, ...)

## S3 method for class 'anova.rms'
plot(x,
     what=c("chisqminusdf","chisq","aic","P","partial R2","remaining R2",
            "proportion R2", "proportion chisq"),
     xlab=NULL, pch=16,
     rm.totals=TRUE, rm.ia=FALSE, rm.other=NULL, newnames,
     sort=c("descending","ascending","none"), margin=c('chisq','P'),
     pl=TRUE, trans=NULL, ntrans=40, height=NULL, width=NULL, ...)

## S3 method for class 'anova.rms'
latex(object, title, dec.chisq=2,
      dec.F=2, dec.ss=NA, dec.ms=NA, dec.P=4, dec.REV=3,
      table.env=TRUE,
      caption=NULL, fontsize=1, params, ...)

## S3 method for class 'anova.rms'
html(object, ...)

Arguments

Details

If the statistics being plotted with plot.anova.rms are few in number and one of them is negative or zero, plot.anova.rms will quit because of an error in dotchart2.

The latex method requires LaTeX packages relsize and needspace.

Value

anova.rms returns a matrix of class anova.rms containing factors as rows and $\chi^2$, d.f., and $P$-values as columns (or d.f., partial $SS, MS, F, P$). An attribute vinfo provides list of variables involved in each row and the type of test done. plot.anova.rms invisibly returns the vector of quantities plotted. This vector has a names attribute describing the terms for which the statistics in the vector are calculated.

Side Effects

print prints, latex creates a file with a name of the form "title.tex" (see the title argument above).

Author(s)

Frank Harrell
Department of Biostatistics, Vanderbilt University
[email protected]

See Also

prmiInfo, rms, rmsMisc, lrtest, rms.trans, summary.rms, plot.Predict, ggplot.Predict, solvet, locator, dotchart2, latex, xYplot, anova.lm, contrast.rms, pantext

Examples

require(ggplot2)
n <- 1000    # define sample size
set.seed(17) # so can reproduce the results
treat <- factor(sample(c('a','b','c'), n,TRUE))
num.diseases <- sample(0:4, n,TRUE)
age <- rnorm(n, 50, 10)
cholesterol <- rnorm(n, 200, 25)
weight <- rnorm(n, 150, 20)
sex <- factor(sample(c('female','male'), n,TRUE))
label(age) <- 'Age'      # label is in Hmisc
label(num.diseases) <- 'Number of Comorbid Diseases'
label(cholesterol) <- 'Total Cholesterol'
label(weight) <- 'Weight, lbs.'
label(sex) <- 'Sex'
units(cholesterol) <- 'mg/dl'   # uses units.default in Hmisc


# Specify population model for log odds that Y=1
L <- .1*(num.diseases-2) + .045*(age-50) +
     (log(cholesterol - 10)-5.2)*(-2*(treat=='a') +
     3.5*(treat=='b')+2*(treat=='c'))
# Simulate binary y to have Prob(y=1) = 1/[1+exp(-L)]
y <- ifelse(runif(n) < plogis(L), 1, 0)


fit <- lrm(y ~ treat + scored(num.diseases) + rcs(age) +
               log(cholesterol+10) + treat:log(cholesterol+10),
           x=TRUE, y=TRUE)   # x, y needed for test='LR'
a <- anova(fit)                       # Test all factors
b <- anova(fit, treat, cholesterol)   # Test these 2 by themselves
                                      # to get their pooled effects
a
b
a2 <- anova(fit, test='LR')
b2 <- anova(fit, treat, cholesterol, test='LR')
a2
b2

# Add a new line to the plot with combined effects
s <- rbind(a2, 'treat+cholesterol'=b2['TOTAL',])

class(s) <- 'anova.rms'
plot(s, margin=c('chisq', 'proportion chisq'))

g <- lrm(y ~ treat*rcs(age))
dd <- datadist(treat, num.diseases, age, cholesterol)
options(datadist='dd')
p <- Predict(g, age, treat="b")
s <- anova(g)
tx <- paste(capture.output(s), collapse='\n')
ggplot(p) + annotate('text', x=27, y=3.2, family='mono', label=tx,
                      hjust=0, vjust=1, size=1.5)

plot(s, margin=c('chisq', 'proportion chisq'))
# new plot - dot chart of chisq-d.f. with 2 other stats in right margin
# latex(s)                       # nice printout - creates anova.g.tex
options(datadist=NULL)


# Simulate data with from a given model, and display exactly which
# hypotheses are being tested


set.seed(123)
age <- rnorm(500, 50, 15)
treat <- factor(sample(c('a','b','c'), 500, TRUE))
bp  <- rnorm(500, 120, 10)
y   <- ifelse(treat=='a', (age-50)*.05, abs(age-50)*.08) + 3*(treat=='c') +
       pmax(bp, 100)*.09 + rnorm(500)
f   <- ols(y ~ treat*lsp(age,50) + rcs(bp,4))
print(names(coef(f)), quote=FALSE)
specs(f)
anova(f)
an <- anova(f)
options(digits=3)
print(an, 'subscripts')
print(an, 'dots')


an <- anova(f, test='Chisq', ss=FALSE)
# plot(0:1)                        # make some plot
# tab <- pantext(an, 1.2, .6, lattice=FALSE, fontfamily='Helvetica')
# create function to write table; usually omit fontfamily
# tab()                            # execute it; could do tab(cex=.65)
plot(an)                         # new plot - dot chart of chisq-d.f.
# Specify plot(an, trans=sqrt) to use a square root scale for this plot
# latex(an)                      # nice printout - creates anova.f.tex


## Example to save partial R^2 for all predictors, along with overall
## R^2, from two separate fits, and to combine them with ggplot2

require(ggplot2)
set.seed(1)
n <- 100
x1 <- runif(n)
x2 <- runif(n)
y  <- (x1-.5)^2 + x2 + runif(n)
group <- c(rep('a', n/2), rep('b', n/2))
A <- NULL
for(g in c('a','b')) {
    f <- ols(y ~ pol(x1,2) + pol(x2,2) + pol(x1,2) %ia% pol(x2,2),
             subset=group==g)
    a <- plot(anova(f),
              what='partial R2', pl=FALSE, rm.totals=FALSE, sort='none')
    a <- a[-grep('NONLINEAR', names(a))]
    d <- data.frame(group=g, Variable=factor(names(a), names(a)),
                    partialR2=unname(a))
    A <- rbind(A, d)
  }
ggplot(A, aes(x=partialR2, y=Variable)) + geom_point() +
       facet_wrap(~ group) + xlab(ex <- expression(partial~R^2)) +
       scale_y_discrete(limits=rev)
ggplot(A, aes(x=partialR2, y=Variable, color=group)) + geom_point() +
       xlab(ex <- expression(partial~R^2)) +
       scale_y_discrete(limits=rev)

# Suppose that a researcher wants to make a big deal about a variable
# because it has the highest adjusted chi-square.  We use the
# bootstrap to derive 0.95 confidence intervals for the ranks of all
# the effects in the model.  We use the plot method for anova, with
# pl=FALSE to suppress actual plotting of chi-square - d.f. for each
# bootstrap repetition.
# It is important to tell plot.anova.rms not to sort the results, or
# every bootstrap replication would have ranks of 1,2,3,... for the stats.

n <- 300
set.seed(1)
d <- data.frame(x1=runif(n), x2=runif(n),  x3=runif(n),
   x4=runif(n), x5=runif(n), x6=runif(n),  x7=runif(n),
   x8=runif(n), x9=runif(n), x10=runif(n), x11=runif(n),
   x12=runif(n))
d$y <- with(d, 1*x1 + 2*x2 + 3*x3 +  4*x4  + 5*x5 + 6*x6 +
               7*x7 + 8*x8 + 9*x9 + 10*x10 + 11*x11 +
              12*x12 + 9*rnorm(n))

f <- ols(y ~ x1+x2+x3+x4+x5+x6+x7+x8+x9+x10+x11+x12, data=d)
B <- 20   # actually use B=1000
ranks <- matrix(NA, nrow=B, ncol=12)
rankvars <- function(fit)
  rank(plot(anova(fit), sort='none', pl=FALSE))
Rank <- rankvars(f)
for(i in 1:B) {
  j <- sample(1:n, n, TRUE)
  bootfit <- update(f, data=d, subset=j)
  ranks[i,] <- rankvars(bootfit)
  }
lim <- t(apply(ranks, 2, quantile, probs=c(.025,.975)))
predictor <- factor(names(Rank), names(Rank))
w <- data.frame(predictor, Rank, lower=lim[,1], upper=lim[,2])
ggplot(w, aes(x=predictor, y=Rank)) + geom_point() + coord_flip() +
  scale_y_continuous(breaks=1:12) +
  geom_errorbar(aes(ymin=lim[,1], ymax=lim[,2]), width=0)

Description

Converts an 'Ocens' object to a data frame so that subsetting will preserve all needed attributes

Usage

## S3 method for class 'Ocens'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

Value

data frame containing a 2-column integer matrix with attributes

Author(s)

Frank Harrell

Description

bj fits the Buckley-James distribution-free least squares multiple regression model to a possibly right-censored response variable. This model reduces to ordinary least squares if there is no censoring. By default, model fitting is done after taking logs of the response variable. bj uses the rms class for automatic anova, fastbw, validate, Function, nomogram, summary, plot, bootcov, and other functions. The bootcov function may be worth using with bj fits, as the properties of the Buckley-James covariance matrix estimator are not fully known for strange censoring patterns.

For the print method, format of output is controlled by the user previously running options(prType="lang") where lang is "plain" (the default), "latex", or "html". When using html with Quarto or RMarkdown, results='asis' need not be written in the chunk header.

The residuals.bj function exists mainly to compute residuals and to censor them (i.e., return them as Surv objects) just as the original failure time variable was censored. These residuals are useful for checking to see if the model also satisfies certain distributional assumptions. To get these residuals, the fit must have specified y=TRUE.

The bjplot function is a special plotting function for objects created by bj with x=TRUE, y=TRUE in effect. It produces three scatterplots for every covariate in the model: the first plots the original situation, where censored data are distingushed from non-censored data by a different plotting symbol. In the second plot, called a renovated plot, vertical lines show how censored data were changed by the procedure, and the third is equal to the second, but without vertical lines. Imputed data are again distinguished from the non-censored by a different symbol.

The validate method for bj validates the Somers' Dxy rank correlation between predicted and observed responses, accounting for censoring.

The primary fitting function for bj is bj.fit, which does not allow missing data and expects a full design matrix as input.

Usage

bj(formula, data=environment(formula), subset, na.action=na.delete,
   link="log", control, method='fit', x=FALSE, y=FALSE, 
   time.inc)

## S3 method for class 'bj'
print(x, digits=4, long=FALSE, coefs=TRUE, 
title="Buckley-James Censored Data Regression", ...)

## S3 method for class 'bj'
residuals(object, type=c("censored","censored.normalized"),...)

bjplot(fit, which=1:dim(X)[[2]])

## S3 method for class 'bj'
validate(fit, method="boot", B=40,
         bw=FALSE,rule="aic",type="residual",sls=.05,aics=0,
         force=NULL, estimates=TRUE, pr=FALSE,
		 tol=1e-7, rel.tolerance=1e-3, maxiter=15, ...)

bj.fit(x, y, control)

Arguments

Details

The program implements the algorithm as described in the original article by Buckley & James. Also, we have used the original Buckley & James prescription for computing variance/covariance estimator. This is based on non-censored observations only and does not have any theoretical justification, but has been shown in simulation studies to behave well. Our experience confirms this view. Convergence is rather slow with this method, so you may want to increase the number of iterations. Our experience shows that often, in particular with high censoring, 100 iterations is not too many. Sometimes the method will not converge, but will instead enter a loop of repeating values (this is due to the discrete nature of Kaplan and Meier estimator and usually happens with small sample sizes). The program will look for such a loop and return the average betas. It will also issue a warning message and give the size of the cycle (usually less than 6).

Value

bj returns a fit object with similar information to what survreg, psm, cph would store as well as what rms stores and units and time.inc. residuals.bj returns a Surv object. One of the components of the fit object produced by bj (and bj.fit) is a vector called stats which contains the following names elements: "Obs", "Events", "d.f.","error d.f.","sigma","g". Here sigma is the estimate of the residual standard deviation. g is the $g$-index. If the link function is "log", the $g$-index on the anti-log scale is also returned as gr.

Author(s)

Janez Stare
Department of Biomedical Informatics
Ljubljana University
Ljubljana, Slovenia
[email protected]

Harald Heinzl
Department of Medical Computer Sciences
Vienna University
Vienna, Austria
[email protected]

Frank Harrell
Department of Biostatistics
Vanderbilt University
[email protected]

References

Buckley JJ, James IR. Linear regression with censored data. Biometrika 1979; 66:429–36.

Miller RG, Halpern J. Regression with censored data. Biometrika 1982; 69: 521–31.

James IR, Smith PJ. Consistency results for linear regression with censored data. Ann Statist 1984; 12: 590–600.

Lai TL, Ying Z. Large sample theory of a modified Buckley-James estimator for regression analysis with censored data. Ann Statist 1991; 19: 1370–402.

Hillis SL. Residual plots for the censored data linear regression model. Stat in Med 1995; 14: 2023–2036.

Jin Z, Lin DY, Ying Z. On least-squares regression with censored data. Biometrika 2006; 93:147–161.

See Also

rms, psm, survreg, cph, Surv, na.delete, na.detail.response, datadist, rcorr.cens, GiniMd, prModFit, dxy.cens

Examples

require(survival)
suppressWarnings(RNGversion("3.5.0"))
set.seed(1)
ftime  <- 10*rexp(200)
stroke <- ifelse(ftime > 10, 0, 1)
ftime  <- pmin(ftime, 10)
units(ftime) <- "Month"
age <- rnorm(200, 70, 10)
hospital <- factor(sample(c('a','b'),200,TRUE))
dd <- datadist(age, hospital)
options(datadist="dd")

# Prior to rms 6.0 and R 4.0 the following worked with 5 knots
f <- bj(Surv(ftime, stroke) ~ rcs(age,3) + hospital, x=TRUE, y=TRUE)
# add link="identity" to use a censored normal regression model instead
# of a lognormal one
anova(f)
fastbw(f)
validate(f, B=15)
plot(Predict(f, age, hospital))
# needs datadist since no explicit age,hosp.
coef(f)               # look at regression coefficients
coef(psm(Surv(ftime, stroke) ~ rcs(age,3) + hospital, dist='lognormal'))
                      # compare with coefficients from likelihood-based
                      # log-normal regression model
                      # use dist='gau' not under R 


r <- resid(f, 'censored.normalized')
survplot(npsurv(r ~ 1), conf='none') 
                      # plot Kaplan-Meier estimate of 
                      # survival function of standardized residuals
survplot(npsurv(r ~ cut2(age, g=2)), conf='none')  
                      # may desire both strata to be n(0,1)
options(datadist=NULL)

Description

This functions constructs an object resembling one produced by the boot package's boot function, and runs that package's boot.ci function to compute BCa and percentile confidence limits. bootBCa can provide separate confidence limits for a vector of statistics when estimate has length greater than 1. In that case, estimates must have the same number of columns as estimate has values.

Usage

bootBCa(estimate, estimates, type=c('percentile','bca','basic'),
               n, seed, conf.int = 0.95)

Arguments

Value

a 2-vector if estimate is of length 1, otherwise a matrix with 2 rows and number of columns equal to the length of estimate

Note

You can use if(!exists('.Random.seed')) runif(1) before running your bootstrap to make sure that .Random.seed will be available to bootBCa.

Author(s)

Frank Harrell

See Also

boot.ci

Examples

## Not run: 
x1 <- runif(100); x2 <- runif(100); y <- sample(0:1, 100, TRUE)
f <- lrm(y ~ x1 + x2, x=TRUE, y=TRUE)
seed <- .Random.seed
b <- bootcov(f)
# Get estimated log odds at x1=.4, x2=.6
X <- cbind(c(1,1), x1=c(.4,2), x2=c(.6,3))
est <- X 
ests <- t(X 
bootBCa(est, ests, n=100, seed=seed)
bootBCa(est, ests, type='bca', n=100, seed=seed)
bootBCa(est, ests, type='basic', n=100, seed=seed)

## End(Not run)

Description

bootcov computes a bootstrap estimate of the covariance matrix for a set of regression coefficients from ols, lrm, cph, psm, Rq, and any other fit where x=TRUE, y=TRUE was used to store the data used in making the original regression fit and where an appropriate fitter function is provided here. The estimates obtained are not conditional on the design matrix, but are instead unconditional estimates. For small sample sizes, this will make a difference as the unconditional variance estimates are larger. This function will also obtain bootstrap estimates corrected for cluster sampling (intra-cluster correlations) when a "working independence" model was used to fit data which were correlated within clusters. This is done by substituting cluster sampling with replacement for the usual simple sampling with replacement. bootcov has an option (coef.reps) that causes all of the regression coefficient estimates from all of the bootstrap re-samples to be saved, facilitating computation of nonparametric bootstrap confidence limits and plotting of the distributions of the coefficient estimates (using histograms and kernel smoothing estimates).

The loglik option facilitates the calculation of simultaneous confidence regions from quantities of interest that are functions of the regression coefficients, using the method of Tibshirani(1996). With Tibshirani's method, one computes the objective criterion (-2 log likelihood evaluated at the bootstrap estimate of $\beta$ but with respect to the original design matrix and response vector) for the original fit as well as for all of the bootstrap fits. The confidence set of the regression coefficients is the set of all coefficients that are associated with objective function values that are less than or equal to say the 0.95 quantile of the vector of B + 1 objective function values. For the coefficients satisfying this condition, predicted values are computed at a user-specified design matrix X, and minima and maxima of these predicted values (over the qualifying bootstrap repetitions) are computed to derive the final simultaneous confidence band.

The bootplot function takes the output of bootcov and either plots a histogram and kernel density estimate of specified regression coefficients (or linear combinations of them through the use of a specified design matrix X), or a qqnorm plot of the quantities of interest to check for normality of the maximum likelihood estimates. bootplot draws vertical lines at specified quantiles of the bootstrap distribution, and returns these quantiles for possible printing by the user. Bootstrap estimates may optionally be transformed by a user-specified function fun before plotting.

The confplot function also uses the output of bootcov but to compute and optionally plot nonparametric bootstrap pointwise confidence limits or (by default) Tibshirani (1996) simultaneous confidence sets. A design matrix must be specified to allow confplot to compute quantities of interest such as predicted values across a range of values or differences in predicted values (plots of effects of changing one or more predictor variable values).

bootplot and confplot are actually generic functions, with the particular functions bootplot.bootcov and confplot.bootcov automatically invoked for bootcov objects.

A service function called histdensity is also provided (for use with bootplot). It runs hist and density on the same plot, using twice the number of classes than the default for hist, and 1.5 times the width than the default used by density.

A comprehensive example demonstrates the use of all of the functions.

When bootstrapping an ordinal model for a numeric Y (when ytarget is not specified), some original distinct Y values are not sampled so there will be fewer intercepts in the model. bootcov linearly interpolates and extrapolates to fill in the missing intercepts so that the intercepts are aligned over bootstrap samples. Also see the Hmisc ordGroupBoot function.

Usage

bootcov(fit, cluster, B=200, fitter, 
        coef.reps=TRUE, loglik=FALSE,
        pr=FALSE, group=NULL, stat=NULL,
        seed=sample(10000, 1), ytarget=NULL, ...)


bootplot(obj, which=1 : ncol(Coef), X,
         conf.int=c(.9,.95,.99),
         what=c('density', 'qqnorm', 'box'),
         fun=function(x) x, labels., ...)


confplot(obj, X, against, 
         method=c('simultaneous','pointwise'),
         conf.int=0.95, fun=function(x)x,
         add=FALSE, lty.conf=2, ...)


histdensity(y, xlab, nclass, width, mult.width=1, ...)

Arguments

Details

If the fit has a scale parameter (e.g., a fit from psm), the log of the individual bootstrap scale estimates are added to the vector of parameter estimates and and column and row for the log scale are added to the new covariance matrix (the old covariance matrix also has this row and column).

For Rq fits, the tau, method, and hs arguments are taken from the original fit.

Value

a new fit object with class of the original object and with the element orig.var added. orig.var is the covariance matrix of the original fit. Also, the original var component is replaced with the new bootstrap estimates. The component boot.coef is also added. This contains the mean bootstrap estimates of regression coefficients (with a log scale element added if applicable). boot.Coef is added if coef.reps=TRUE. boot.loglik is added if loglik=TRUE. If stat is specified an additional vector boot.stats will be contained in the returned object. B contains the number of successfully fitted bootstrap resamples. A component clusterInfo is added to contain elements name and n holding the name of the cluster variable and the number of clusters.

bootplot returns a (possible matrix) of quantities of interest and the requested quantiles of them. confplot returns three vectors: fitted, lower, and upper.

Side Effects

bootcov prints if pr=TRUE

Author(s)

Frank Harrell
Department of Biostatistics
Vanderbilt University
[email protected]

Bill Pikounis
Biometrics Research Department
Merck Research Laboratories
https://billpikounis.com/wpb/

References

Feng Z, McLerran D, Grizzle J (1996): A comparison of statistical methods for clustered data analysis with Gaussian error. Stat in Med 15:1793–1806.

Tibshirani R, Knight K (1996): Model search and inference by bootstrap "bumping". Department of Statistics, University of Toronto. Technical report available from
http://www-stat.stanford.edu/~tibs/. Presented at the Joint Statistical Meetings, Chicago, August 1996.

See Also

ordGroupBoot, robcov, sample, rms, lm.fit, lrm.fit, orm.fit, survival-internal, predab.resample, rmsMisc, Predict, gendata, contrast.rms, Predict, setPb, multiwayvcov::cluster.boot

Examples

set.seed(191)
x <- exp(rnorm(200))
logit <- 1 + x/2
y <- ifelse(runif(200) <= plogis(logit), 1, 0)
f <- lrm(y ~ pol(x,2), x=TRUE, y=TRUE)
g <- bootcov(f, B=50, pr=TRUE, seed=3)
anova(g)    # using bootstrap covariance estimates
fastbw(g)   # using bootstrap covariance estimates
beta <- g$boot.Coef[,1]
hist(beta, nclass=15)     #look at normality of parameter estimates
qqnorm(beta)
# bootplot would be better than these last two commands


# A dataset contains a variable number of observations per subject,
# and all observations are laid out in separate rows. The responses
# represent whether or not a given segment of the coronary arteries
# is occluded. Segments of arteries may not operate independently
# in the same patient.  We assume a "working independence model" to
# get estimates of the coefficients, i.e., that estimates assuming
# independence are reasonably efficient.  The job is then to get
# unbiased estimates of variances and covariances of these estimates.


set.seed(2)
n.subjects <- 30
ages <- rnorm(n.subjects, 50, 15)
sexes  <- factor(sample(c('female','male'), n.subjects, TRUE))
logit <- (ages-50)/5
prob <- plogis(logit)  # true prob not related to sex
id <- sample(1:n.subjects, 300, TRUE) # subjects sampled multiple times
table(table(id))  # frequencies of number of obs/subject
age <- ages[id]
sex <- sexes[id]
# In truth, observations within subject are independent:
y   <- ifelse(runif(300) <= prob[id], 1, 0)
f <- lrm(y ~ lsp(age,50)*sex, x=TRUE, y=TRUE)
g <- bootcov(f, id, B=50, seed=3)  # usually do B=200 or more
diag(g$var)/diag(f$var)
# add ,group=w to re-sample from within each level of w
anova(g)            # cluster-adjusted Wald statistics
# fastbw(g)         # cluster-adjusted backward elimination
plot(Predict(g, age=30:70, sex='female'))  # cluster-adjusted confidence bands


# Get design effects based on inflation of the variances when compared
# with bootstrap estimates which ignore clustering
g2 <- bootcov(f, B=50, seed=3)
diag(g$var)/diag(g2$var)


# Get design effects based on pooled tests of factors in model
anova(g2)[,1] / anova(g)[,1]


# Simulate binary data where there is a strong 
# age x sex interaction with linear age effects 
# for both sexes, but where not knowing that
# we fit a quadratic model.  Use the bootstrap
# to get bootstrap distributions of various
# effects, and to get pointwise and simultaneous
# confidence limits


set.seed(71)
n   <- 500
age <- rnorm(n, 50, 10)
sex <- factor(sample(c('female','male'), n, rep=TRUE))
L   <- ifelse(sex=='male', 0, .1*(age-50))
y   <- ifelse(runif(n)<=plogis(L), 1, 0)


f <- lrm(y ~ sex*pol(age,2), x=TRUE, y=TRUE)
b <- bootcov(f, B=50, loglik=TRUE, pr=TRUE, seed=3)   # better: B=500


par(mfrow=c(2,3))
# Assess normality of regression estimates
bootplot(b, which=1:6, what='qq')
# They appear somewhat non-normal


# Plot histograms and estimated densities 
# for 6 coefficients
w <- bootplot(b, which=1:6)
# Print bootstrap quantiles
w$quantiles

# Show box plots for bootstrap reps for all coefficients
bootplot(b, what='box')


# Estimate regression function for females
# for a sequence of ages
ages <- seq(25, 75, length=100)
label(ages) <- 'Age'


# Plot fitted function and pointwise normal-
# theory confidence bands
par(mfrow=c(1,1))
p <- Predict(f, age=ages, sex='female')
plot(p)
# Save curve coordinates for later automatic
# labeling using labcurve in the Hmisc library
curves <- vector('list',8)
curves[[1]] <- with(p, list(x=age, y=lower))
curves[[2]] <- with(p, list(x=age, y=upper))


# Add pointwise normal-distribution confidence 
# bands using unconditional variance-covariance
# matrix from the 500 bootstrap reps
p <- Predict(b, age=ages, sex='female')
curves[[3]] <- with(p, list(x=age, y=lower))
curves[[4]] <- with(p, list(x=age, y=upper))


dframe <- expand.grid(sex='female', age=ages)
X <- predict(f, dframe, type='x')  # Full design matrix


# Add pointwise bootstrap nonparametric 
# confidence limits
p <- confplot(b, X=X, against=ages, method='pointwise',
              add=TRUE, lty.conf=4)
curves[[5]] <- list(x=ages, y=p$lower)
curves[[6]] <- list(x=ages, y=p$upper)


# Add simultaneous bootstrap confidence band
p <- confplot(b, X=X, against=ages, add=TRUE, lty.conf=5)
curves[[7]] <- list(x=ages, y=p$lower)
curves[[8]] <- list(x=ages, y=p$upper)
lab <- c('a','a','b','b','c','c','d','d')
labcurve(curves, lab, pl=TRUE)


# Now get bootstrap simultaneous confidence set for
# female:male odds ratios for a variety of ages


dframe <- expand.grid(age=ages, sex=c('female','male'))
X <- predict(f, dframe, type='x')  # design matrix
f.minus.m <- X[1:100,] - X[101:200,]
# First 100 rows are for females.  By subtracting
# design matrices are able to get Xf*Beta - Xm*Beta
# = (Xf - Xm)*Beta


confplot(b, X=f.minus.m, against=ages,
         method='pointwise', ylab='F:M Log Odds Ratio')
confplot(b, X=f.minus.m, against=ages,
         lty.conf=3, add=TRUE)


# contrast.rms makes it easier to compute the design matrix for use
# in bootstrapping contrasts:


f.minus.m <- contrast(f, list(sex='female',age=ages),
                         list(sex='male',  age=ages))$X
confplot(b, X=f.minus.m)


# For a quadratic binary logistic regression model use bootstrap
# bumping to estimate coefficients under a monotonicity constraint
set.seed(177)
n <- 400
x <- runif(n)
logit <- 3*(x^2-1)
y <- rbinom(n, size=1, prob=plogis(logit))
f <- lrm(y ~ pol(x,2), x=TRUE, y=TRUE)
k <- coef(f)
k
vertex <- -k[2]/(2*k[3])
vertex


# Outside [0,1] so fit satisfies monotonicity constraint within
# x in [0,1], i.e., original fit is the constrained MLE


g <- bootcov(f, B=50, coef.reps=TRUE, loglik=TRUE, seed=3)
bootcoef <- g$boot.Coef    # 100x3 matrix
vertex <- -bootcoef[,2]/(2*bootcoef[,3])
table(cut2(vertex, c(0,1)))
mono <- !(vertex >= 0 & vertex <= 1)
mean(mono)    # estimate of Prob{monotonicity in [0,1]}


var(bootcoef)   # var-cov matrix for unconstrained estimates
var(bootcoef[mono,])   # for constrained estimates


# Find second-best vector of coefficient estimates, i.e., best
# from among bootstrap estimates
g$boot.Coef[order(g$boot.loglik[-length(g$boot.loglik)])[1],]
# Note closeness to MLE

## Not run: 
# Get the bootstrap distribution of the difference in two ROC areas for
# two binary logistic models fitted on the same dataset.  This analysis
# does not adjust for the bias ROC area (C-index) due to overfitting.
# The same random number seed is used in two runs to enforce pairing.

set.seed(17)
x1 <- rnorm(100)
x2 <- rnorm(100)
y <- sample(0:1, 100, TRUE)
f <- lrm(y ~ x1, x=TRUE, y=TRUE)
g <- lrm(y ~ x1 + x2, x=TRUE, y=TRUE)
f <- bootcov(f, stat='C', seed=4)
g <- bootcov(g, stat='C', seed=4)
dif <- g$boot.stats - f$boot.stats
hist(dif)
quantile(dif, c(.025,.25,.5,.75,.975))
# Compute a z-test statistic.  Note that comparing ROC areas is far less
# powerful than likelihood or Brier score-based methods
z <- (g$stats['C'] - f$stats['C'])/sd(dif)
names(z) <- NULL
c(z=z, P=2*pnorm(-abs(z)))

# For an ordinal y with some distinct values of y not very popular, let
# bootcov use linear extrapolation to fill in intercepts for non-sampled levels

f <- orm(y ~ x1 + x2, x=TRUE, y=TRUE)
bootcov(f, B=200)

# Instead of filling in missing intercepts, perform minimum binning so that
# there is a 0.9999 probability that all distinct Y values will be represented
# in bootstrap samples
y <- ordGroupBoot(y)
f <- orm(y ~ x1 + x2, x=TRUE, y=TRUE)
bootcov(f, B=200)

# Instead just keep one intercept for all bootstrap fits - the intercept
# that pertains to y=10

bootcov(f, B=200, ytarget=10)   # use ytarget=NA for the median

## End(Not run)

Description

Uses lattice graphics and the output from Predict to plot image, contour, or perspective plots showing the simultaneous effects of two continuous predictor variables. Unless formula is provided, the $x$-axis is constructed from the first variable listed in the call to Predict and the $y$-axis variable comes from the second.

The perimeter function is used to generate the boundary of data to plot when a 3-d plot is made. It finds the area where there are sufficient data to generate believable interaction fits.

Usage

bplot(x, formula, lfun=lattice::levelplot, xlab, ylab, zlab,
      adj.subtitle=!info$ref.zero, cex.adj=.75, cex.lab=1,
      perim, showperim=FALSE,
      zlim=range(yhat, na.rm=TRUE), scales=list(arrows=FALSE),
      xlabrot, ylabrot, zlabrot=90, ...)

perimeter(x, y, xinc=diff(range(x))/10, n=10, lowess.=TRUE)

Arguments

Details

perimeter is a kind of generalization of datadist for 2 continuous variables. First, the n smallest and largest x values are determined. These form the lowest and highest possible xs to display. Then x is grouped into intervals bounded by these two numbers, with the interval widths defined by xinc. Within each interval, y is sorted and the $n$th smallest and largest y are taken as the interval containing sufficient data density to plot interaction surfaces. The interval is ignored when there are insufficient y values. When the data are being readied for persp, bplot uses the approx function to do linear interpolation of the y-boundaries as a function of the x values actually used in forming the grid (the values of the first variable specified to Predict). To make the perimeter smooth, specify lowess.=TRUE to perimeter.

Value

perimeter returns a matrix of class perimeter. This outline can be conveniently plotted by lines.perimeter.

Author(s)

Frank Harrell
Department of Biostatistics, Vanderbilt University
[email protected]

See Also

datadist, Predict, rms, rmsMisc, levelplot, contourplot, wireframe

Examples

n <- 1000    # define sample size
set.seed(17) # so can reproduce the results
age            <- rnorm(n, 50, 10)
blood.pressure <- rnorm(n, 120, 15)
cholesterol    <- rnorm(n, 200, 25)
sex            <- factor(sample(c('female','male'), n,TRUE))
label(age)            <- 'Age'      # label is in Hmisc
label(cholesterol)    <- 'Total Cholesterol'
label(blood.pressure) <- 'Systolic Blood Pressure'
label(sex)            <- 'Sex'
units(cholesterol)    <- 'mg/dl'   # uses units.default in Hmisc
units(blood.pressure) <- 'mmHg'

# Specify population model for log odds that Y=1
L <- .4*(sex=='male') + .045*(age-50) +
  (log(cholesterol - 10)-5.2)*(-2*(sex=='female') + 2*(sex=='male'))
# Simulate binary y to have Prob(y=1) = 1/[1+exp(-L)]
y <- ifelse(runif(n) < plogis(L), 1, 0)

ddist <- datadist(age, blood.pressure, cholesterol, sex)
options(datadist='ddist')

fit <- lrm(y ~ blood.pressure + sex * (age + rcs(cholesterol,4)),
               x=TRUE, y=TRUE)
p <- Predict(fit, age, cholesterol, sex, np=50) # vary sex last
require(lattice)
bplot(p)                 # image plot for age, cholesterol with color
                         # coming from yhat; use default ranges for
                         # both continuous predictors; two panels (for sex)
bplot(p, lfun=wireframe) # same as bplot(p,,wireframe)
# View from different angle, change y label orientation accordingly
# Default is z=40, x=-60
bplot(p,, wireframe, screen=list(z=40, x=-75), ylabrot=-25)
bplot(p,, contourplot)   # contour plot
bounds  <- perimeter(age, cholesterol, lowess=TRUE)
plot(age, cholesterol)     # show bivariate data density and perimeter
lines(bounds[,c('x','ymin')]); lines(bounds[,c('x','ymax')])
p <- Predict(fit, age, cholesterol)  # use only one sex
bplot(p, perim=bounds)   # draws image() plot
                         # don't show estimates where data are sparse
                         # doesn't make sense here since vars don't interact
bplot(p, plogis(yhat) ~ age*cholesterol) # Probability scale
options(datadist=NULL)

Description

Uses bootstrapping or cross-validation to get bias-corrected (overfitting- corrected) estimates of predicted vs. observed values based on subsetting predictions into intervals (for survival models) or on nonparametric smoothers (for other models). There are calibration functions for Cox (cph), parametric survival models (psm), binary and ordinal logistic models (lrm) and ordinary least squares (ols). For survival models, "predicted" means predicted survival probability at a single time point, and "observed" refers to the corresponding Kaplan-Meier survival estimate, stratifying on intervals of predicted survival, or, if the polspline package is installed, the predicted survival probability as a function of transformed predicted survival probability using the flexible hazard regression approach (see the val.surv function for details). For logistic and linear models, a nonparametric calibration curve is estimated over a sequence of predicted values. The fit must have specified x=TRUE, y=TRUE. The print and plot methods for lrm and ols models (which use calibrate.default) print the mean absolute error in predictions, the mean squared error, and the 0.9 quantile of the absolute error. Here, error refers to the difference between the predicted values and the corresponding bias-corrected calibrated values.

Below, the second, third, and fourth invocations of calibrate are, respectively, for ols and lrm, cph, and psm. The first and second plot invocation are respectively for lrm and ols fits or all other fits.

Usage

calibrate(fit, ...)
## Default S3 method:
calibrate(fit, predy, 
  method=c("boot","crossvalidation",".632","randomization"),
  B=40, bw=FALSE, rule=c("aic","p"),
  type=c("residual","individual"),
  sls=.05, aics=0, force=NULL, estimates=TRUE, pr=FALSE, kint,
  smoother="lowess", digits=NULL, ...) 
## S3 method for class 'cph'
calibrate(fit, cmethod=c('hare', 'KM'),
  method="boot", u, m=150, pred, cuts, B=40, 
  bw=FALSE, rule="aic", type="residual", sls=0.05, aics=0, force=NULL,
  estimates=TRUE,
  pr=FALSE, what="observed-predicted", tol=1e-12, maxdim=5, ...)
## S3 method for class 'psm'
calibrate(fit, cmethod=c('hare', 'KM'),
  method="boot", u, m=150, pred, cuts, B=40,
  bw=FALSE,rule="aic",
  type="residual", sls=.05, aics=0, force=NULL, estimates=TRUE,
  pr=FALSE, what="observed-predicted", tol=1e-12, maxiter=15, 
  rel.tolerance=1e-5, maxdim=5, ...)

## S3 method for class 'calibrate'
print(x, B=Inf, ...)
## S3 method for class 'calibrate.default'
print(x, B=Inf, ...)

## S3 method for class 'calibrate'
plot(x, xlab, ylab, subtitles=TRUE, conf.int=TRUE,
 cex.subtitles=.75, riskdist=TRUE, add=FALSE,
 scat1d.opts=list(nhistSpike=200), par.corrected=NULL, ...)

## S3 method for class 'calibrate.default'
plot(x, xlab, ylab, xlim, ylim,
  legend=TRUE, subtitles=TRUE, cex.subtitles=.75, riskdist=TRUE,
  scat1d.opts=list(nhistSpike=200), ...)

Arguments

Details

If the fit was created using penalized maximum likelihood estimation, the same penalty and penalty.scale parameters are used during validation.

Value

matrix specifying mean predicted survival in each interval, the corresponding estimated bias-corrected Kaplan-Meier estimates, number of subjects, and other statistics. For linear and logistic models, the matrix instead has rows corresponding to the prediction points, and the vector of predicted values being validated is returned as an attribute. The returned object has class "calibrate" or "calibrate.default". plot.calibrate.default invisibly returns the vector of estimated prediction errors corresponding to the dataset used to fit the model.

Side Effects

prints, and stores an object pred.obs or .orig.cal

Author(s)

Frank Harrell
Department of Biostatistics
Vanderbilt University
[email protected]

See Also

validate, predab.resample, groupkm, errbar, scat1d, cph, psm, lowess,fit.mult.impute, processMI

Examples

require(survival)
set.seed(1)
n <- 200
d.time <- rexp(n)
x1 <- runif(n)
x2 <- factor(sample(c('a', 'b', 'c'), n, TRUE))
f <- cph(Surv(d.time) ~ pol(x1,2) * x2, x=TRUE, y=TRUE, surv=TRUE, time.inc=1.5)
#or f <- psm(S ~ \dots)
pa <- requireNamespace('polspline')
if(pa) {
 cal <- calibrate(f, u=1.5, B=20)  # cmethod='hare'
 plot(cal)
}
cal <- calibrate(f, u=1.5, cmethod='KM', m=50, B=20)  # usually B=200 or 300
plot(cal, add=pa)

set.seed(1)
y <- sample(0:2, n, TRUE)
x1 <- runif(n)
x2 <- runif(n)
x3 <- runif(n)
x4 <- runif(n)
f <- lrm(y ~ x1 + x2 + x3 * x4, x=TRUE, y=TRUE)
cal <- calibrate(f, kint=2, predy=seq(.2, .8, length=60), 
                 group=y)
# group= does k-sample validation: make resamples have same 
# numbers of subjects in each level of y as original sample

plot(cal)
#See the example for the validate function for a method of validating
#continuation ratio ordinal logistic models.  You can do the same
#thing for calibrate

Description

This function computes one or more contrasts of the estimated regression coefficients in a fit from one of the functions in rms, along with standard errors, confidence limits, t or Z statistics, P-values. General contrasts are handled by obtaining the design matrix for two sets of predictor settings (a, b) and subtracting the corresponding rows of the two design matrics to obtain a new contrast design matrix for testing the a - b differences. This allows for quite general contrasts (e.g., estimated differences in means between a 30 year old female and a 40 year old male). This can also be used to obtain a series of contrasts in the presence of interactions (e.g., female:male log odds ratios for several ages when the model contains age by sex interaction). Another use of contrast is to obtain center-weighted (Type III test) and subject-weighted (Type II test) estimates in a model containing treatment by center interactions. For the latter case, you can specify type="average" and an optional weights vector to average the within-center treatment contrasts. The design contrast matrix computed by contrast.rms can be used by other functions.

When the model was fitted by a Bayesian function such as blrm, highest posterior density intervals for contrasts are computed instead, along with the posterior probability that the contrast is positive. posterior.summary specifies whether posterior mean/median/mode is to be used for contrast point estimates.

contrast.rms also allows one to specify four settings to contrast, yielding contrasts that are double differences - the difference between the first two settings (a - b) and the last two (a2 - b2). This allows assessment of interactions.

If usebootcoef=TRUE, the fit was run through bootcov, and conf.type="individual", the confidence intervals are bootstrap nonparametric percentile confidence intervals, basic bootstrap, or BCa intervals, obtained on contrasts evaluated on all bootstrap samples.

By omitting the b argument, contrast can be used to obtain an average or weighted average of a series of predicted values, along with a confidence interval for this average. This can be useful for "unconditioning" on one of the predictors (see the next to last example).

Specifying type="joint", and specifying at least as many contrasts as needed to span the space of a complex test, one can make multiple degree of freedom tests flexibly and simply. Redundant contrasts will be ignored in the joint test. See the examples below. These include an example of an "incomplete interaction test" involving only two of three levels of a categorical variable (the test also tests the main effect).

When more than one contrast is computed, the list created by contrast.rms is suitable for plotting (with error bars or bands) with xYplot or Dotplot (see the last example before the type="joint" examples).

When fit is the result of a Bayesian model fit and fun is specified, contrast.rms operates altogether differently. a and b must both be specified and a2, b2 not specified. fun is evaluated on the estimates separately on a and b and the subtraction is deferred. So even in the absence of interactions, when fun is nonlinear, the settings of factors (predictors) will not cancel out and estimates of differences will be covariate-specific (unless there are no covariates in the model besides the one being varied to get from a to b).

That the the use of offsets to compute profile confidence intervals prevents this function from working with certain models that use offsets for other purposes, e.g., Poisson models with offsets to account for population size.

Usage

contrast(fit, ...)
## S3 method for class 'rms'
contrast(fit, a, b, a2, b2, ycut=NULL, cnames=NULL,
         fun=NULL, funint=TRUE,
         type=c("individual", "average", "joint"),
         conf.type=c("individual","simultaneous","profile"), usebootcoef=TRUE,
         boot.type=c("percentile","bca","basic"),
         posterior.summary=c('mean', 'median', 'mode'),
         weights="equal", conf.int=0.95, tol=1e-7, expand=TRUE,
         se_factor=4, plot_profile=FALSE, ...)
## S3 method for class 'contrast.rms'
print(x, X=FALSE,
       fun=function(u)u, jointonly=FALSE, prob=0.95, ...)

Arguments

Value

a list of class "contrast.rms" containing the elements Contrast, SE, Z, var, df.residual Lower, Upper, Pvalue, X, cnames, redundant, which denote the contrast estimates, standard errors, Z or t-statistics, variance matrix, residual degrees of freedom (this is NULL if the model was not ols), lower and upper confidence limits, 2-sided P-value, design matrix, contrast names (or NULL), and a logical vector denoting which contrasts are redundant with the other contrasts. If there are any redundant contrasts, when the results of contrast are printed, and asterisk is printed at the start of the corresponding lines. The object also contains ctype indicating what method was used for compute confidence intervals.

Author(s)

Frank Harrell
Department of Biostatistics
Vanderbilt University School of Medicine
[email protected]

See Also

Predict, gendata, bootcov, summary.rms, anova.rms,

Examples

require(ggplot2)
set.seed(1)
age <- rnorm(200,40,12)
sex <- factor(sample(c('female','male'),200,TRUE))
logit <- (sex=='male') + (age-40)/5
y <- ifelse(runif(200) <= plogis(logit), 1, 0)
f <- lrm(y ~ pol(age,2)*sex)
anova(f)
# Compare a 30 year old female to a 40 year old male
# (with or without age x sex interaction in the model)
contrast(f, list(sex='female', age=30), list(sex='male', age=40))
# Test for interaction between age and sex, duplicating anova
contrast(f, list(sex='female', age=30),
            list(sex='male',   age=30),
            list(sex='female', age=c(40,50)),
            list(sex='male',   age=c(40,50)), type='joint')
# Duplicate overall sex effect in anova with 3 d.f.
contrast(f, list(sex='female', age=c(30,40,50)),
            list(sex='male',   age=c(30,40,50)), type='joint')
# For females get an array of odds ratios against age=40
k <- contrast(f, list(sex='female', age=30:50),
                 list(sex='female', age=40))
print(k, fun=exp)
# Plot odds ratios with pointwise 0.95 confidence bands using log scale
k <- as.data.frame(k[c('Contrast','Lower','Upper')])
ggplot(k, aes(x=30:50, y=exp(Contrast))) + geom_line() +
   geom_ribbon(aes(ymin=exp(Lower), ymax=exp(Upper)),
               alpha=0.15, linetype=0) +
   scale_y_continuous(trans='log10', n.breaks=10,
               minor_breaks=c(seq(0.1, 1, by=.1), seq(1, 10, by=.5))) +
  xlab('Age') + ylab('OR against age 40')

# For an ordinal model with 3 variables (x1 is quadratic, x2 & x3 linear)
# Get a 1 d.f. likelihood ratio (LR) test for x1=1 vs x1=0.25
# For the other variables get contrasts and LR tests that are the
# ordinary ones for their original coefficients.
# Get 0.95 profile likelihood confidence intervals for the x1 contrast
# and for the x2 and x3 coefficients
set.seed(7)
x1 <- runif(50)
x2 <- runif(50)
x3 <- runif(50)
dd <- datadist(x1, x2, x3); options(datadist='dd')
y <- x1 + runif(50)   # need x=TRUE,y=TRUE for profile likelihood
f <- orm(y ~ pol(x1, 2) + x2 + x3, x=TRUE, y=TRUE)
a <- list(x1=c(   1,0,0), x2=c(0,1,0), x3=c(0,0,1))
b <- list(x1=c(0.25,0,0), x2=c(0,0,0), x3=c(0,0,0))
k <- contrast(f, a, b, expand=FALSE)      # Wald intervals and tests
k; k$X[1,]
summary(f, x1=c(.25, 1), x2=0:1, x3=0:1)  # Wald intervals
anova(f, test='LR')                       # LR tests
contrast(f, a, b, expand=FALSE, conf.type='profile', plot_profile=TRUE)
options(datadist=NULL)


# For a model containing two treatments, centers, and treatment
# x center interaction, get 0.95 confidence intervals separately
# by center
center <- factor(sample(letters[1 : 8], 500, TRUE))
treat  <- factor(sample(c('a','b'), 500, TRUE))
y      <- 8*(treat == 'b') + rnorm(500, 100, 20)
f <- ols(y ~ treat*center)


lc <- levels(center)
contrast(f, list(treat='b', center=lc),
            list(treat='a', center=lc))


# Get 'Type III' contrast: average b - a treatment effect over
# centers, weighting centers equally (which is almost always
# an unreasonable thing to do)
contrast(f, list(treat='b', center=lc),
            list(treat='a', center=lc),
         type='average')


# Get 'Type II' contrast, weighting centers by the number of
# subjects per center.  Print the design contrast matrix used.
k <- contrast(f, list(treat='b', center=lc),
                 list(treat='a', center=lc),
              type='average', weights=table(center))
print(k, X=TRUE)
# Note: If other variables had interacted with either treat
# or center, we may want to list settings for these variables
# inside the list()'s, so as to not use default settings


# For a 4-treatment study, get all comparisons with treatment 'a'
treat  <- factor(sample(c('a','b','c','d'),  500, TRUE))
y      <- 8*(treat == 'b') + rnorm(500, 100, 20)
dd     <- datadist(treat, center); options(datadist='dd')
f <- ols(y ~ treat*center)
lt <- levels(treat)
contrast(f, list(treat=lt[-1]),
            list(treat=lt[ 1]),
         cnames=paste(lt[-1], lt[1], sep=':'), conf.int=1 - .05 / 3)


# Compare each treatment with average of all others
for(i in 1 : length(lt)) {
  cat('Comparing with', lt[i], '\n\n')
  print(contrast(f, list(treat=lt[-i]),
                    list(treat=lt[ i]), type='average'))
}
options(datadist=NULL)

# Six ways to get the same thing, for a variable that
# appears linearly in a model and does not interact with
# any other variables.  We estimate the change in y per
# unit change in a predictor x1.  Methods 4, 5 also
# provide confidence limits.  Method 6 computes nonparametric
# bootstrap confidence limits.  Methods 2-6 can work
# for models that are nonlinear or non-additive in x1.
# For that case more care is needed in choice of settings
# for x1 and the variables that interact with x1.


## Not run: 
coef(fit)['x1']                            # method 1
diff(predict(fit, gendata(x1=c(0,1))))     # method 2
g <- Function(fit)                         # method 3
g(x1=1) - g(x1=0)
summary(fit, x1=c(0,1))                    # method 4
k <- contrast(fit, list(x1=1), list(x1=0)) # method 5
print(k, X=TRUE)
fit <- update(fit, x=TRUE, y=TRUE)         # method 6
b <- bootcov(fit, B=500)
contrast(fit, list(x1=1), list(x1=0))


# In a model containing age, race, and sex,
# compute an estimate of the mean response for a
# 50 year old male, averaged over the races using
# observed frequencies for the races as weights


f <- ols(y ~ age + race + sex)
contrast(f, list(age=50, sex='male', race=levels(race)),
         type='average', weights=table(race))

# For a Bayesian model get the highest posterior interval for the
# difference in two nonlinear functions of predicted values
# Start with the mean from a proportional odds model
g <- blrm(y ~ x)
M <- Mean(g)
contrast(g, list(x=1), list(x=0), fun=M)

# For the median we have to make sure that contrast can pass the
# per-posterior-draw vector of intercepts through
qu <- Quantile(g)
med <- function(lp, intercepts) qu(0.5, lp, intercepts=intercepts)
contrast(g, list(x=1), list(x=0), fun=med)

## End(Not run)


# Plot the treatment effect (drug - placebo) as a function of age
# and sex in a model in which age nonlinearly interacts with treatment
# for females only

set.seed(1)
n <- 800
treat <- factor(sample(c('drug','placebo'), n,TRUE))
sex   <- factor(sample(c('female','male'),  n,TRUE))
age   <- rnorm(n, 50, 10)
y     <- .05*age + (sex=='female')*(treat=='drug')*.05*abs(age-50) + rnorm(n)
f     <- ols(y ~ rcs(age,4)*treat*sex)
d     <- datadist(age, treat, sex); options(datadist='d')

# show separate estimates by treatment and sex

require(ggplot2)
ggplot(Predict(f, age, treat, sex='female'))
ggplot(Predict(f, age, treat, sex='male'))
ages  <- seq(35,65,by=5); sexes <- c('female','male')
w     <- contrast(f, list(treat='drug',    age=ages, sex=sexes),
                     list(treat='placebo', age=ages, sex=sexes))
# add conf.type="simultaneous" to adjust for having done 14 contrasts
xYplot(Cbind(Contrast, Lower, Upper) ~ age | sex, data=w,
       ylab='Drug - Placebo')
w <- as.data.frame(w[c('age','sex','Contrast','Lower','Upper')])
ggplot(w, aes(x=age, y=Contrast)) + geom_point() + facet_grid(sex ~ .) +
   geom_errorbar(aes(ymin=Lower, ymax=Upper), width=0)
ggplot(w, aes(x=age, y=Contrast)) + geom_line() + facet_grid(sex ~ .) +
   geom_ribbon(aes(ymin=Lower, ymax=Upper), width=0, alpha=0.15, linetype=0)
xYplot(Cbind(Contrast, Lower, Upper) ~ age, groups=sex, data=w,
       ylab='Drug - Placebo', method='alt bars')
options(datadist=NULL)


# Examples of type='joint' contrast tests

set.seed(1)
x1 <- rnorm(100)
x2 <- factor(sample(c('a','b','c'), 100, TRUE))
dd <- datadist(x1, x2); options(datadist='dd')
y  <- x1 + (x2=='b') + rnorm(100)

# First replicate a test statistic from anova()

f <- ols(y ~ x2)
anova(f)
contrast(f, list(x2=c('b','c')), list(x2='a'), type='joint')

# Repeat with a redundancy; compare a vs b, a vs c, b vs c

contrast(f, list(x2=c('a','a','b')), list(x2=c('b','c','c')), type='joint')

# Get a test of association of a continuous predictor with y
# First assume linearity, then cubic

f <- lrm(y>0 ~ x1 + x2)
anova(f)
contrast(f, list(x1=1), list(x1=0), type='joint')  # a minimum set of contrasts
xs <- seq(-2, 2, length=20)
contrast(f, list(x1=0), list(x1=xs), type='joint')

# All contrasts were redundant except for the first, because of
# linearity assumption

f <- lrm(y>0 ~ pol(x1,3) + x2, x=TRUE, y=TRUE)
anova(f)
anova(f, test='LR')   # discrepancy with Wald statistics points out a problem w/them

contrast(f, list(x1=0), list(x1=xs), type='joint')
print(contrast(f, list(x1=0), list(x1=xs), type='joint'), jointonly=TRUE)

# All contrasts were redundant except for the first 3, because of
# cubic regression assumption
# These Wald tests and intervals are not very accurate.  Although joint
# testing is not implemented in contrast(), individual profile likelihood
# confidence intervals and associted likelihood ratio tests are helpful:
# contrast(f, list(x1=0), list(x1=xs), conf.type='profile', plot_profile=TRUE)

# Now do something that is difficult to do without cryptic contrast
# matrix operations: Allow each of the three x2 groups to have a different
# shape for the x1 effect where x1 is quadratic.  Test whether there is
# a difference in mean levels of y for x2='b' vs. 'c' or whether
# the shape or slope of x1 is different between x2='b' and x2='c' regardless
# of how they differ when x2='a'.  In other words, test whether the mean
# response differs between group b and c at any value of x1.
# This is a 3 d.f. test (intercept, linear, quadratic effects) and is
# a better approach than subsetting the data to remove x2='a' then
# fitting a simpler model, as it uses a better estimate of sigma from
# all the data.

f <- ols(y ~ pol(x1,2) * x2)
anova(f)
contrast(f, list(x1=xs, x2='b'),
            list(x1=xs, x2='c'), type='joint')

# Note: If using a spline fit, there should be at least one value of
# x1 between any two knots and beyond the outer knots.
options(datadist=NULL)

Description

Modification of Therneau's coxph function to fit the Cox model and its extension, the Andersen-Gill model. The latter allows for interval time-dependent covariables, time-dependent strata, and repeated events. The Survival method for an object created by cph returns an S function for computing estimates of the survival function. The Quantile method for cph returns an S function for computing quantiles of survival time (median, by default). The Mean method returns a function for computing the mean survival time. This function issues a warning if the last follow-up time is uncensored, unless a restricted mean is explicitly requested.

Usage

cph(formula = formula(data), data=environment(formula),
    weights, subset, na.action=na.delete, 
    method=c("efron","breslow","exact","model.frame","model.matrix"), 
    singular.ok=FALSE, robust=FALSE,
    model=FALSE, x=FALSE, y=FALSE, se.fit=FALSE,
    linear.predictors=TRUE, residuals=TRUE, nonames=FALSE,
    eps=1e-4, init, iter.max=10, tol=1e-9, surv=FALSE, time.inc,
    type=NULL, vartype=NULL, debug=FALSE, ...)

## S3 method for class 'cph'
Survival(object, ...)
# Evaluate result as g(times, lp, stratum=1, type=c("step","polygon"))

## S3 method for class 'cph'
Quantile(object, ...)
# Evaluate like h(q, lp, stratum=1, type=c("step","polygon"))

## S3 method for class 'cph'
Mean(object, method=c("exact","approximate"), type=c("step","polygon"),
          n=75, tmax, ...)
# E.g. m(lp, stratum=1, type=c("step","polygon"), tmax, \dots)

Arguments

Details

If there is any strata by covariable interaction in the model such that the mean X beta varies greatly over strata, method="approximate" may not yield very accurate estimates of the mean in Mean.cph.

For method="approximate" if you ask for an estimate of the mean for a linear predictor value that was outside the range of linear predictors stored with the fit, the mean for that observation will be NA.

Value

For Survival, Quantile, or Mean, an S function is returned. Otherwise, in addition to what is listed below, formula/design information and the components maxtime, time.inc, units, model, x, y, se.fit are stored, the last 5 depending on the settings of options by the same names. The vectors or matrix stored if y=TRUE or x=TRUE have rows deleted according to subset and to missing data, and have names or row names that come from the data frame used as input data.

Author(s)

Frank Harrell
Department of Biostatistics, Vanderbilt University
[email protected]

See Also

coxph, survival-internal, Surv, residuals.cph, cox.zph, survfit.cph, survest.cph, survfit.coxph, survplot, datadist, rms, rms.trans, anova.rms, summary.rms, Predict, fastbw, validate, calibrate, plot.Predict, ggplot.Predict, specs.rms, lrm, which.influence, na.delete, na.detail.response, print.cph, latex.cph, vif, ie.setup, GiniMd, dxy.cens, concordance

Examples

# Simulate data from a population model in which the log hazard
# function is linear in age and there is no age x sex interaction

require(survival)
require(ggplot2)
n <- 1000
set.seed(731)
age <- 50 + 12*rnorm(n)
label(age) <- "Age"
sex <- factor(sample(c('Male','Female'), n, 
              rep=TRUE, prob=c(.6, .4)))
cens <- 15*runif(n)
h <- .02*exp(.04*(age-50)+.8*(sex=='Female'))
dt <- -log(runif(n))/h
label(dt) <- 'Follow-up Time'
e <- ifelse(dt <= cens,1,0)
dt <- pmin(dt, cens)
units(dt) <- "Year"
dd <- datadist(age, sex)
options(datadist='dd')
S <- Surv(dt,e)

f <- cph(S ~ rcs(age,4) + sex, x=TRUE, y=TRUE)
cox.zph(f, "rank")             # tests of PH
anova(f)
ggplot(Predict(f, age, sex)) # plot age effect, 2 curves for 2 sexes
survplot(f, sex)             # time on x-axis, curves for x2
res <- resid(f, "scaledsch")
time <- as.numeric(dimnames(res)[[1]])
z <- loess(res[,4] ~ time, span=0.50)   # residuals for sex
plot(time, fitted(z))
lines(supsmu(time, res[,4]),lty=2)
plot(cox.zph(f,"identity"))    #Easier approach for last few lines
# latex(f)


f <- cph(S ~ age + strat(sex), surv=TRUE)
g <- Survival(f)   # g is a function
g(seq(.1,1,by=.1), stratum="sex=Male", type="poly") #could use stratum=2
med <- Quantile(f)
plot(Predict(f, age, fun=function(x) med(lp=x)))  #plot median survival

# Fit a model that is quadratic in age, interacting with sex as strata
# Compare standard errors of linear predictor values with those from
# coxph
# Use more stringent convergence criteria to match with coxph

f <- cph(S ~ pol(age,2)*strat(sex), x=TRUE, eps=1e-9, iter.max=20)
coef(f)
se <- predict(f, se.fit=TRUE)$se.fit
require(lattice)
xyplot(se ~ age | sex, main='From cph')
a <- c(30,50,70)
comb <- data.frame(age=rep(a, each=2),
                   sex=rep(levels(sex), 3))

p <- predict(f, comb, se.fit=TRUE)
comb$yhat  <- p$linear.predictors
comb$se    <- p$se.fit
z <- qnorm(.975)
comb$lower <- p$linear.predictors - z*p$se.fit
comb$upper <- p$linear.predictors + z*p$se.fit
comb

age2 <- age^2
f2 <- coxph(S ~ (age + age2)*strata(sex))
coef(f2)
se <- predict(f2, se.fit=TRUE)$se.fit
xyplot(se ~ age | sex, main='From coxph')
comb <- data.frame(age=rep(a, each=2), age2=rep(a, each=2)^2,
                   sex=rep(levels(sex), 3))
p <- predict(f2, newdata=comb, se.fit=TRUE)
comb$yhat <- p$fit
comb$se   <- p$se.fit
comb$lower <- p$fit - z*p$se.fit
comb$upper <- p$fit + z*p$se.fit
comb


# g <- cph(Surv(hospital.charges) ~ age, surv=TRUE)
# Cox model very useful for analyzing highly skewed data, censored or not
# m <- Mean(g)
# m(0)                           # Predicted mean charge for reference age


#Fit a time-dependent covariable representing the instantaneous effect
#of an intervening non-fatal event
rm(age)
set.seed(121)
dframe <- data.frame(failure.time=1:10, event=rep(0:1,5),
                     ie.time=c(NA,1.5,2.5,NA,3,4,NA,5,5,5), 
                     age=sample(40:80,10,rep=TRUE))
z <- ie.setup(dframe$failure.time, dframe$event, dframe$ie.time)
S <- z$S
ie.status <- z$ie.status
attach(dframe[z$subs,])    # replicates all variables

f <- cph(S ~ age + ie.status, x=TRUE, y=TRUE)  
#Must use x=TRUE,y=TRUE to get survival curves with time-dep. covariables


#Get estimated survival curve for a 50-year old who has an intervening
#non-fatal event at 5 days
new <- data.frame(S=Surv(c(0,5), c(5,999), c(FALSE,FALSE)), age=rep(50,2),
                  ie.status=c(0,1))
g <- survfit(f, new)
plot(c(0,g$time), c(1,g$surv[,2]), type='s', 
     xlab='Days', ylab='Survival Prob.')
# Not certain about what columns represent in g$surv for survival5
# but appears to be for different ie.status
#or:
#g <- survest(f, new)
#plot(g$time, g$surv, type='s', xlab='Days', ylab='Survival Prob.')


#Compare with estimates when there is no intervening event
new2 <- data.frame(S=Surv(c(0,5), c(5, 999), c(FALSE,FALSE)), age=rep(50,2),
                   ie.status=c(0,0))
g2 <- survfit(f, new2)
lines(c(0,g2$time), c(1,g2$surv[,2]), type='s', lty=2)
#or:
#g2 <- survest(f, new2)
#lines(g2$time, g2$surv, type='s', lty=2)
detach("dframe[z$subs, ]")
options(datadist=NULL)