## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(risdr)

## ----data---------------------------------------------------------------------
set.seed(2026)
X <- matrix(rnorm(100 * 8), nrow = 100)
colnames(X) <- paste0("Gene", seq_len(ncol(X)))
event_time <- rexp(100, rate = exp(0.5 * X[, 1] - 0.3 * X[, 2]))
censor_time <- rexp(100, rate = 0.5)
time <- pmin(event_time, censor_time)
delta <- as.integer(event_time <= censor_time)
table(delta)

## ----fit----------------------------------------------------------------------
fit <- fit_risdr_sparse(
  X, time, delta = delta, response_type = "survival",
  sdr_method = "sir", cov_method = "oas", nslices = 4,
  d_max = 3, selector = "cicomp", lambda_grid = c(0, 0.05, 0.1),
  support_penalty = FALSE
)
c(d = fit$d, lambda = fit$best_lambda)
fit$selection_grid

## ----support------------------------------------------------------------------
B <- fit$directions
support <- data.frame(variable = rownames(B), norm = sqrt(rowSums(B^2)))
support <- support[support$norm > 1e-8, , drop = FALSE]
support[order(support$norm, decreasing = TRUE), , drop = FALSE]

## ----dual---------------------------------------------------------------------
dual <- fit_risdr_dual(
  X, time, delta = delta, response_type = "survival",
  dual_rank = 5, d_max = 3, sdr_method = "sir", cov_method = "oas",
  lambda_grid = c(0, 0.05, 0.1), gene_select_top = 4, verbose = FALSE
)
dual$selected_variables

## ----prediction---------------------------------------------------------------
predict(fit, X[1:2, , drop = FALSE], type = "scores")
predict(fit, X[1:2, , drop = FALSE], type = "risk")
predict(dual, X[1:2, , drop = FALSE], type = "scores")

## ----ph-----------------------------------------------------------------------
survival::cox.zph(fit$downstream_fit, transform = "km", terms = TRUE, global = TRUE)