BART: surv.pre.bart – R documentation

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surv.pre.bart

Data construction for survival analysis with BART

Description

Survival data contained in (t,δ, x) must be translated to data suitable for the BART survival analysis model; see surv.bart for more details.

Usage

surv.pre.bart( times, delta, x.train=NULL, x.test=NULL,
               K=NULL, events=NULL, ztimes=NULL, zdelta=NULL )

Arguments

`times`	The time of event or right-censoring.
`delta`	The event indicator: 1 is an event while 0 is censored.
`x.train`	Explanatory variables for training (in sample) data. If provided, must be a matrix with (as usual) rows corresponding to observations and columns to variables.
`x.test`	Explanatory variables for test (out of sample) data. If provided, must be a matrix and have the same structure as x.train.
`K`	If provided, then coarsen `times` per the quantiles 1/K, 2/K, ..., K/K.
`events`	If provided, then use for the grid of time points.
`ztimes`	If provided, then these columns of `x.train` (and `x.test` if any) are the times for time-dependent covariates. They will be transformed into time-dependent covariate sojourn times.
`zdelta`	If provided, then these columns of `x.train` (and `x.test` if any) are the delta for time-dependent covariates. They will be transformed into time-dependent covariate binary events.

Value

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

`y.train`	A vector of binary responses.
`tx.train`	A matrix with rows consisting of time and the covariates of the training data.
`tx.test`	A matrix with rows consisting of time and the covariates of the test data, if any.

Examples

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

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

summary(times)
table(delta)

x.train <- as.matrix(lung[group, c(4, 5, 7)]) ## matrix of observed covariates
## lung$age:        Age in years
## lung$sex:        Male=1 Female=2
## lung$ph.karno:   Karnofsky performance score (dead=0:normal=100:by=10)
##                  rated by physician

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

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

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

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

i <- 1

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

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

BART

Bayesian Additive Regression Trees

v2.9

GPL (>= 2)

Authors

Robert McCulloch [aut], Rodney Sparapani [aut, cre], Charles Spanbauer [aut], Robert Gramacy [aut], Matthew Pratola [aut], Martyn Plummer [ctb], Nicky Best [ctb], Kate Cowles [ctb], Karen Vines [ctb]

Initial release

2020-12-21