# Predict method for iCoxBoost fits Obtains predictions at specified boosting steps from a iCoxBoost object fitted by `iCoxBoost`. ```r ## S3 method for class 'iCoxBoost' predict( object, newdata = NULL, subset = NULL, at.step = NULL, times = NULL, type = c("lp", "logplik", "risk", "CIF"), ... ) ``` ## Arguments - `object`: fitted CoxBoost object from a `CoxBoost` call. - `newdata`: data frame with new covariate values (for `n.new` observations). If just prediction for the training data is wanted, it can be omitted. If the predictive partial log-likelihood is wanted (`type=logplik`), this frame also has to contain the response information. - `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 `type="risk"` is used, only one step is admissible. If no step is given, the final boosting step is used. - `times`: vector with `T` time points where prediction is wanted. Only needed for `type="risk"` - `type`: type of prediction to be returned: `"lp"` gives the linear predictor, `"logplik"` the partial log-likelihood, `"risk"` the predicted probability of not yet having had the event at the time points given in `times`, and `"CIF"` the predicted cumulative incidence function, i.e., the predicted probability of having had the event of interest. - ``...``: miscellaneous arguments, none of which is used at the moment. ## Returns 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. ## Examples ```r 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)) ``` ## Author(s) Harald Binder binderh@uni-mainz.de

predict.iCoxBoost function