# Obtain predicted conditional survival and cumulative hazard functions from a global survival stacking object ```r ## S3 method for class 'stackG' predict( object, newX, newtimes, surv_form = object$surv_form, time_grid_approx = object$time_grid_approx, ... ) ``` ## Arguments - `object`: Object of class `stackG` - `newX`: `m x p` data.frame of new observed covariate values at which to obtain `m` predictions for the estimated algorithm. Must have the same names and structure as `X`. - `newtimes`: `k x 1` numeric vector of times at which to obtain `k` predicted conditional survivals. - `surv_form`: Mapping from hazard estimate to survival estimate. Can be either `"PI"` (product integral mapping) or `"exp"` (exponentiated cumulative hazard estimate). Defaults to the value saved in `object`. - `time_grid_approx`: Numeric vector of times at which to approximate product integral or cumulative hazard interval. Defaults to the value saved in `object`. - `...`: Further arguments passed to or from other methods. ## Returns A named list with the following components: - **S_T_preds**: An `m x k` matrix of estimated event time survival probabilities at the `m` covariate vector values and `k` times provided by the user in `newX` and `newtimes`, respectively. - **S_C_preds**: An `m x k` matrix of estimated censoring time survival probabilities at the `m` covariate vector values and `k` times provided by the user in `newX` and `newtimes`, respectively. - **Lambda_T_preds**: An `m x k` matrix of estimated event time cumulative hazard function values at the `m` covariate vector values and `k` times provided by the user in `newX` and `newtimes`, respectively. - **Lambda_C_preds**: An `m x k` matrix of estimated censoring time cumulative hazard function values at the `m` covariate vector values and `k` times provided by the user in `newX` and `newtimes`, respectively. - **time_grid_approx**: The approximation grid for the product integral or cumulative hazard integral, (user-specified). - **surv_form**: Exponential or product-integral form (user-specified). ## Examples ```r # This is a small simulation example set.seed(123) n <- 250 X <- data.frame(X1 = rnorm(n), X2 = rbinom(n, size = 1, prob = 0.5)) S0 <- function(t, x){ pexp(t, rate = exp(-2 + x[,1] - x[,2] + .5 * x[,1] * x[,2]), lower.tail = FALSE) } T <- rexp(n, rate = exp(-2 + X[,1] - X[,2] + .5 * X[,1] * X[,2])) G0 <- function(t, x) { as.numeric(t < 15) *.9*pexp(t, rate = exp(-2 -.5*x[,1]-.25*x[,2]+.5*x[,1]*x[,2]), lower.tail=FALSE) } C <- rexp(n, exp(-2 -.5 * X[,1] - .25 * X[,2] + .5 * X[,1] * X[,2])) C[C > 15] <- 15 entry <- runif(n, 0, 15) time <- pmin(T, C) event <- as.numeric(T <= C) sampled <- which(time >= entry) X <- X[sampled,] time <- time[sampled] event <- event[sampled] entry <- entry[sampled] # Note that this a very small Super Learner library, for computational purposes. SL.library <- c("SL.mean", "SL.glm") fit <- stackG(time = time, event = event, entry = entry, X = X, newX = X, newtimes = seq(0, 15, .1), direction = "prospective", bin_size = 0.1, time_basis = "continuous", time_grid_approx = sort(unique(time)), surv_form = "exp", learner = "SuperLearner", SL_control = list(SL.library = SL.library, V = 5)) preds <- predict(object = fit, newX = X, newtimes = seq(0, 15, 0.1)) plot(preds$S_T_preds[1,], S0(t = seq(0, 15, .1), X[1,])) abline(0,1,col='red') ``` ## See Also stackG

predict.stackG function