Structured Screen-and-Select Variable Selection in Linear, Generalized Linear, and Survival Models.

S3VS

The goal of S3VS is to perform variable selection using the structured screen-and-select (S3VS) framework in linear models, generalized linear models with binary data, and survival models such as the Cox model and accelerated failure time (AFT) model.

Installation

You can install the development version of S3VS like so:

devtools::install_github("nilotpalsanyal/S3VS")

Description

The central entry point is S3VS(), which dispatches to a family-specific routine via the argument family:

• S3VS_LM() for linear models,
• S3VS_GLM() for generalized linear models with binary outcomes,
• S3VS_SURV() for survival models.

The S3VS workflow proceeds through the following steps, each handled by helper functions:

• Stopping rule check:looprun() determines whether the iterative screen-and-select process should continue.

• Leading variable identification:get_leadvars() identifies leading variables; family-specific versions are get_leadvars_LM, get_leadvars_GLM, and get_leadvars_SURV.

• Leading set identification:get_leadsets() identifies the leading set for each leading variable.

• Selection within leading sets:VS_method() performs selection within leading sets; family-specific methods include VS_method_LM(), VS_method_GLM(), VS_method_SURV(), and bridge_aft() implements BRIDGE specifically for AFT models.

• Aggregation of selected variables:select_vars() retains promising variables as selected from an iteration.

• Aggregation of non-selected variables: (optional) remove_vars() removes variables deemed uninformative from future iterations (if no variable is selected in the current iteration by select_vars().

• Response update: (optional) update_y() enables iterative response updates; family-specific variants includeupdate_y_LM() andupdate_y_GLM().

Together, these functions form a structured, iterative pipeline for efficient variable screening and selection in high-dimensional regression and survival analysis.

• Prediction:pred_S3VS() produces predictions using variables selected by S3VS, calling pred_S3VS_LM(), pred_S3VS_GLM(), or pred_S3VS_SURV() as appropriate.