# Predicted Survival Probabilities Using S3VS-Selected Predictors in Generalized Linear Models `pred_S3VS` returns predicted survival probabilities using predictors selected by S3VS in generalized linear models. ```r pred_S3VS_SURV(y, X, surv_model = c("AFT", "COX"), method = c("AFTREG", "AFTGEE"), times) ``` ## Arguments - `y`: Response. A list with components `time` and `status` (1 = event, 0 = censored). - `X`: Predictor matrix. This should include predictors selected by S3VS. Can be a base matrix or something `as.matrix()` can coerce. No missing values are allowed. - `surv_model`: Character string specifying the survival model. Must be explicitly provided; there is no default. Values are `"Cox"` for proportional hazards models, `"AFT"` for accelerated failure time models. - `method`: Character string indicating the prediction method used. Available options are `"COXGLMNET"` for `surv_model = "COX"` and `"AFTREG"` and `"AFTGEE"` for `surv_model = "AFT"`. See Details for more information. - `times`: Vector of time points where predicted survival probabilities will be computed. ## Returns A list containing: - **y.pred**: Predicted response - **coef**: Coefficient estimates of the predictors used for prediction ## Author(s) Nilotpal Sanyal , Padmore N. Prempeh ## See Also `cv.glmnet`, `coxph`, `aftreg`, `aftgee` ## Examples ```r # Simulate survival data (Cox) set.seed(123) n <- 100 p <- 150 X <- matrix(rnorm(n * p), n, p) colnames(X) <- paste0("V", 1:p) eta <- X[,1] + 0.5 * X[,2] base_rate <- 0.05 T_event <- rexp(n, rate = base_rate * exp(eta)) C <- rexp(n, rate = 0.03) time <- pmin(T_event, C) status <- as.integer(T_event <= C) y_surv <- list(time = time, status = status) # Run S3VS for linear models res_surv <- S3VS(y = y_surv, X = X, family = "survival", surv_model = "COX", vsel_method = "COXGLMNET", method_xy = "topk", param_xy = list(k = 1), method_xx = "topk", param_xx = list(k = 3), method_sel = "conservative", method_rem = "conservative_begin", sel_regout = FALSE, rem_regout = FALSE, m = 100, nskip = 3, verbose = TRUE, seed = 123) pred_surv <- pred_S3VS_SURV(y = y_surv, X = X[,res_surv$selected], surv_model = "COX", method = "COXGLMNET") pred_surv ```

pred_S3VS_SURV function