crisk.pre.bart {BART} | R Documentation |
Competing risks contained in (t, δ, x) must be translated to data
suitable for the BART competing risks model; see crisk.bart
for more details.
crisk.pre.bart( times, delta, x.train=NULL, x.test=NULL, x.train2=x.train, x.test2=x.test, K=NULL )
times |
The time of event or right-censoring. |
delta |
The event indicator: 1 is a cause 1 event, 2 a cause 2 while 0 is censored. |
x.train |
Explanatory variables for training (in sample) data of cause 1. |
x.test |
Explanatory variables for test (out of sample) data of cause 1. |
x.train2 |
Explanatory variables for training (in sample) data of cause 2. |
x.test2 |
Explanatory variables for test (out of sample) data of cause 2. |
K |
If provided, then coarsen |
surv.pre.bart
returns a list.
Besides the items listed below, the list has
a times
component giving the unique times and K
which is the number of
unique times.
y.train |
A vector of binary responses for cause 1. |
y.train2 |
A vector of binary responses for cause 2. |
cond |
A vector of indices of |
binaryOffset |
The binary offset for |
binaryOffset2 |
The binary offset for |
tx.train |
A matrix with rows consisting of time and the covariates of the training data for cause 1. |
tx.train2 |
A matrix with rows consisting of time and the covariates of the training data for cause 2. |
tx.test |
A matrix with rows consisting of time and the covariates of the test data, if any, for cause 1. |
tx.test2 |
A matrix with rows consisting of time and the covariates of the test data, if any, for cause 2. |
Rodney Sparapani: rsparapa@mcw.edu
Sparapani, R., Logan, B., McCulloch, R., and Laud, P. (2016) Nonparametric survival analysis using Bayesian Additive Regression Trees (BART). Statistics in Medicine, 16:2741-53 <doi:10.1002/sim.6893>.
data(transplant) delta <- (as.numeric(transplant$event)-1) delta[delta==1] <- 4 delta[delta==2] <- 1 delta[delta>1] <- 2 table(delta, transplant$event) table(1+floor(transplant$futime/30.5)) ## months times <- 1+floor(transplant$futime/30.5) typeO <- 1*(transplant$abo=='O') typeA <- 1*(transplant$abo=='A') typeB <- 1*(transplant$abo=='B') typeAB <- 1*(transplant$abo=='AB') table(typeA, typeO) x.train <- cbind(typeO, typeA, typeB, typeAB) N <- nrow(x.train) x.test <- x.train x.test[1:N, 1:4] <- matrix(c(1, 0, 0, 0), nrow=N, ncol=4, byrow=TRUE) pre <- crisk.pre.bart(x.train=x.train, times=times, delta=delta, x.test=x.test)