# Bridge-Penalized AFT Regression via Iteratively Reweighted LASSO `bridge_aft` fits an accelerated failure time (AFT) model using an iterative reweighted LASSO scheme to approximate a bridge ($L_\gamma$) penalty on the regression coefficients. ```r bridge_aft(y, X, gamma = 0.5, alpha = 1, max_iter = 100, tol = 1e-05) ``` ## Arguments - `y`: Response; a list of two elements `time` and `status`. - `X`: Predictor matrix. Can be a base matrix or something `as.matrix()` can coerce. No missing values are allowed. - `gamma`: Bridge penalty exponent $\gamma$ (default `0.5`). Values in $(0,1]$ approximate nonconvex penalties; $\gamma=1$ reduces to LASSO-like weighting. - `alpha`: Elastic-net mixing parameter passed to `glmnet::cv.glmnet` (default `1` for LASSO; `0` is ridge; `(0,1)` is elastic net). - `max_iter`: Maximum number of outer reweighting iterations (default `100`). - `tol`: Convergence tolerance on the $L_1$ change in coefficients between iterations (default `1e-5`). ## Returns A list with components: - **beta**: Numeric vector of estimated coefficients of length $p$. - **gamma**: The bridge exponent used in the fit. - **iterations**: Number of outer reweighting iterations performed. ## References Jian Huang and Shuangge Ma. Variable selection in the accelerated failure time model via the bridge method. Lifetime Data Analysis, 16(2):176-195, 2010. ## Author(s) Padmore Prempeh , Nilotpal Sanyal ## See Also `glmnet`, `cv.glmnet`, `Surv`, `survfit` ## Examples ```r set.seed(1) n <- 50 p <- 10 X <- matrix(rnorm(n * p), n, p) beta_true <- c(runif(10, -1.5, 1.5), rep(0, p - 10)) linpred <- as.vector(X %*% beta_true) ## Generate log-normal AFT survival times (no censoring in this simple example) sigma <- 0.6 logT <- linpred + rnorm(n, sd = sigma) time <- exp(logT) delta <- rep(1, n) # all events (censoring ignored by current implementation) y_surv <- list(time = time, status = delta) fit <- bridge_aft(y_surv, X, gamma = 0.5, alpha = 1, max_iter = 50, tol = 1e-5) str(fit) fit$beta[1:10] ```

bridge_aft function