S3VS-package.RdPerforms 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.
The S3VS package implements the Structured Screen-and-Select Variable Selection (S3VS) framework for linear models, generalized linear models with binary responses, and survival models (Cox proportional hazards and accelerated failure time models).
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:
looprun determines whether the iterative screen-and-select process should continue.
get_leadvars identifies leading variables; family-specific versions are get_leadvars_LM, get_leadvars_GLM, and get_leadvars_SURV.
get_leadsets identifies the leading set for each leading variable.
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.
select_vars retains promising variables as selected from an iteration.
remove_vars removes variables deemed uninformative from future iterations (if no variable is selected in the current iteration by select_vars).
update_y enables iterative response updates; family-specific variants include update_y_LM and update_y_GLM.
Together, these functions form a structured, iterative pipeline for efficient variable screening and selection in high-dimensional regression and survival analysis.
pred_S3VS produces predictions using variables selected by S3VS, calling pred_S3VS_LM, pred_S3VS_GLM, or pred_S3VS_SURV as appropriate.