1 - ovarian dataset
library(survival)
library(rvflnet)
data(ovarian)
## Warning in data(ovarian): data set 'ovarian' not found
X <- as.matrix(ovarian[, c("age", "resid.ds", "rx", "ecog.ps")])
y <- Surv(ovarian$futime, ovarian$fustat)
set.seed(123)
n <- nrow(X)
train_idx <- sample(1:n, size = round(0.8 * n))
X_train <- X[train_idx, ]
X_test <- X[-train_idx, ]
y_train <- y[train_idx]
y_test <- y[-train_idx]
# -------------------------
# Fit model
# -------------------------
cv_fit <- cv.rvflnet(
X_train, y_train,
n_hidden = 200,
activation = "sigmoid",
family = "cox",
nfolds = 5,
type.measure = "C"
)
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
# -------------------------
# CV performance
# -------------------------
cv_min <- cv_fit$cvfit$cvm[cv_fit$cvfit$lambda == cv_fit$cvfit$lambda.min]
cv_1se <- cv_fit$cvfit$cvm[cv_fit$cvfit$lambda == cv_fit$cvfit$lambda.1se]
cat("\n===========================\n")
##
## ===========================
cat("Cross-Validation (C-index)\n")
## Cross-Validation (C-index)
cat("===========================\n")
## ===========================
cat(sprintf("lambda.min CV C-index: %.4f\n", cv_min))
## lambda.min CV C-index: 0.7914
cat(sprintf("lambda.1se CV C-index: %.4f\n", cv_1se))
## lambda.1se CV C-index: 0.7914
# -------------------------
# Predictions (linear predictor)
# -------------------------
lp_min <- as.numeric(predict(cv_fit, X_test, s = "lambda.min"))
lp_1se <- as.numeric(predict(cv_fit, X_test, s = "lambda.1se"))
# -------------------------
# Robust C-index (auto-fix sign)
# -------------------------
c_index_safe <- function(y, lp) {
c1 <- concordance(y ~ lp)$concordance
c2 <- concordance(y ~ I(-lp))$concordance
max(c1, c2)
}
c_min <- c_index_safe(y_test, lp_min)
c_1se <- c_index_safe(y_test, lp_1se)
# -------------------------
# Sparsity
# -------------------------
coef_min <- coef(cv_fit, s = "lambda.min")
coef_1se <- coef(cv_fit, s = "lambda.1se")
p <- ncol(X_train)
# -------------------------
# Output
# -------------------------
cat("\n========================================\n")
##
## ========================================
cat("MODEL COMPARISON: lambda.min vs lambda.1se\n")
## MODEL COMPARISON: lambda.min vs lambda.1se
cat("========================================\n")
## ========================================
cat(sprintf("\nlambda.min:\n"))
##
## lambda.min:
cat(sprintf(" C-index: %.4f\n", c_min))
## C-index: 0.8571
cat(sprintf(" Non-zero: %d / %d\n", sum(coef_min[-1] != 0), length(coef_min) - 1))
## Non-zero: 10 / 203
cat(sprintf(" Original features used: %d\n", sum(coef_min[2:(p+1)] != 0)))
## Original features used: 0
cat(sprintf(" Hidden features used: %d\n", sum(coef_min[(p+2):length(coef_min)] != 0)))
## Hidden features used: 10
cat(sprintf("\nlambda.1se:\n"))
##
## lambda.1se:
cat(sprintf(" C-index: %.4f\n", c_1se))
## C-index: 0.8571
cat(sprintf(" Non-zero: %d / %d\n", sum(coef_1se[-1] != 0), length(coef_1se) - 1))
## Non-zero: 10 / 203
cat(sprintf(" Original features used: %d\n", sum(coef_1se[2:(p+1)] != 0)))
## Original features used: 0
cat(sprintf(" Hidden features used: %d\n", sum(coef_1se[(p+2):length(coef_1se)] != 0)))
## Hidden features used: 10
cat("\n========================================\n")
##
## ========================================
## RECOMMENDATION
cat("========================================\n")
## ========================================
if(c_1se >= c_min) {
cat("✅ Use lambda.1se - better generalization\n")
cat(sprintf(" (C-index: %.4f vs %.4f)\n", c_1se, c_min))
} else {
cat("⚠️ Use lambda.min - but watch for overfitting\n")
cat(sprintf(" (C-index: %.4f vs %.4f)\n", c_min, c_1se))
}
## ✅ Use lambda.1se - better generalization
## (C-index: 0.8571 vs 0.8571)
2 - PBC dataset
library(survival)
library(rvflnet)
library(glmnet)
# -------------------------
# Data: PBC
# -------------------------
data(pbc)
df <- na.omit(pbc)
# Survival object (death = status == 2)
y <- Surv(df$time, df$status == 2)
# -------------------------
# Design matrix (correct handling of factors)
# -------------------------
X <- model.matrix(~ . - time - status - id, data = df)[, -1]
# -------------------------
# Train/test split
# -------------------------
set.seed(123)
n <- nrow(X)
train_idx <- sample(1:n, size = round(0.8 * n))
X_train <- X[train_idx, ]
X_test <- X[-train_idx, ]
y_train <- y[train_idx]
y_test <- y[-train_idx]
# -------------------------
# Scale WITHOUT leakage
# -------------------------
mu <- colMeans(X_train)
sd <- apply(X_train, 2, sd)
X_train <- scale(X_train, center = mu, scale = sd)
X_test <- scale(X_test, center = mu, scale = sd)
# -------------------------
# Fit RVFL Cox model
# -------------------------
cv_fit <- cv.rvflnet(
X_train, y_train,
n_hidden = 200,
activation = "tanh",
W_type = "sobol",
family = "cox",
nfolds = 5,
type.measure = "C"
)
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: from glmnet C++ code (error code -83); Convergence for 83th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
# -------------------------
# CV performance
# -------------------------
cv_min <- cv_fit$cvfit$cvm[cv_fit$cvfit$lambda == cv_fit$cvfit$lambda.min]
cv_1se <- cv_fit$cvfit$cvm[cv_fit$cvfit$lambda == cv_fit$cvfit$lambda.1se]
cat("\n===========================\n")
##
## ===========================
cat("Cross-Validation (C-index)\n")
## Cross-Validation (C-index)
cat("===========================\n")
## ===========================
cat(sprintf("lambda.min CV C-index: %.4f\n", cv_min))
## lambda.min CV C-index: 0.8306
cat(sprintf("lambda.1se CV C-index: %.4f\n", cv_1se))
## lambda.1se CV C-index: 0.8252
# -------------------------
# Predictions
# -------------------------
lp_min <- as.numeric(predict(cv_fit, X_test, s = "lambda.min"))
lp_1se <- as.numeric(predict(cv_fit, X_test, s = "lambda.1se"))
# -------------------------
# Robust C-index (fix sign ambiguity)
# -------------------------
c_index_safe <- function(y, lp) {
max(
concordance(y ~ lp)$concordance,
concordance(y ~ I(-lp))$concordance
)
}
c_min <- c_index_safe(y_test, lp_min)
c_1se <- c_index_safe(y_test, lp_1se)
# -------------------------
# Output
# -------------------------
cat("\n===========================\n")
##
## ===========================
cat("RVFL Cox on PBC dataset\n")
## RVFL Cox on PBC dataset
cat("===========================\n")
## ===========================
cat(sprintf("\nlambda.min C-index: %.4f\n", c_min))
##
## lambda.min C-index: 0.7856
cat(sprintf("lambda.1se C-index: %.4f\n", c_1se))
## lambda.1se C-index: 0.7856
cat("\nInterpretation:\n")
##
## Interpretation:
if (c_1se > c_min) {
cat("✔ lambda.1se generalizes better\n")
} else {
cat("✔ lambda.min fits better (possible overfitting)\n")
}
## ✔ lambda.min fits better (possible overfitting)
3 - CoxExample dataset
library(rvflnet)
library(survival)
data(CoxExample)
x <- CoxExample$x
y <- CoxExample$y
y[1:5, ]
## time status
## [1,] 1.76877757 1
## [2,] 0.54528404 1
## [3,] 0.04485918 0
## [4,] 0.85032298 0
## [5,] 0.61488426 1
y <- Surv(y[, 1], y[, 2])
fit <- rvflnet(x, y, family = "cox")
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
head(coef(fit, s = 0.05))
## 6 x 1 sparse Matrix of class "dgCMatrix"
## 1
## X1 0.28924335
## X2 -0.01658442
## X3 -0.02362666
## X4 0.08984631
## X5 .
## X6 -0.35248733
set.seed(1)
cvfit <- cv.rvflnet(x, y, family = "cox", type.measure = "C")
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## Warning: Starting in glmnet 5.1, the default Cox tie-handling method will
## change from 'breslow' to 'efron' (matching survival::coxph). To silence this
## message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To
## preview the v5.1 behavior, pass cox.ties = 'efron'.
## [1] 0.03057865
## [1] 0.1354824
# -------------------------
# CV performance
# -------------------------
(cv_min <- cvfit$cvfit$cvm[cv_fit$cvfit$lambda == cv_fit$cvfit$lambda.min])
## [1] 0.7212025
(cv_1se <- cvfit$cvfit$cvm[cv_fit$cvfit$lambda == cv_fit$cvfit$lambda.1se])
## [1] 0.6755981