predict.iCoxBoost {CoxBoost} | R Documentation |
Obtains predictions at specified boosting steps from a iCoxBoost object fitted by iCoxBoost
.
## S3 method for class 'iCoxBoost' predict(object,newdata=NULL, subset=NULL,at.step=NULL,times=NULL, type=c("lp","logplik","risk","CIF"),...)
object |
fitted CoxBoost object from a |
newdata |
data frame with new covariate values (for |
subset |
an optional vector specifying a subset of observations to be used for evaluation. |
at.step |
scalar or vector of boosting step(s) at which prediction is wanted. If |
times |
vector with |
type |
type of prediction to be returned: |
... |
miscellaneous arguments, none of which is used at the moment. |
For type="lp"
and type="logplik"
a vector of length n.new
(at.step
being a scalar) or a n.new * length(at.step)
matrix (at.step
being a vector) with predictions is returned.
For type="risk"
or type="CIF"
a n.new * T
matrix with predicted probabilities at the specific time points is returned.
Harald Binder binderh@uni-mainz.de
n <- 200; p <- 100 beta <- c(rep(1,2),rep(0,p-2)) x <- matrix(rnorm(n*p),n,p) actual.data <- as.data.frame(x) real.time <- -(log(runif(n)))/(10*exp(drop(x %*% beta))) cens.time <- rexp(n,rate=1/10) actual.data$status <- ifelse(real.time <= cens.time,1,0) actual.data$time <- ifelse(real.time <= cens.time,real.time,cens.time) # define training and test set train.index <- 1:100 test.index <- 101:200 # Fit a Cox proportional hazards model by iCoxBoost cbfit <- iCoxBoost(Surv(time,status) ~ .,data=actual.data[train.index,], stepno=300,cv=FALSE) # mean partial log-likelihood for test set in every boosting step step.logplik <- predict(cbfit,newdata=actual.data[test.index,], at.step=0:300,type="logplik") plot(step.logplik) # names of covariates with non-zero coefficients at boosting step # with maximal test set partial log-likelihood print(coef(cbfit,at.step=which.max(step.logplik)-1))