Use a cross validated penalized maximum likelihood generalized linear model to perform feature selection

Usage

shrink(data, threshold = c("one", "all"), plot = FALSE, ...)

Arguments

data: feature_calculations object containing the raw feature matrix produced by theft::calculate_features
threshold: character denoting whether to retain features that have at least one non-zero coefficient "one" across all group levels or features that have non-zero coefficients across all group levels "all". Applicable to multinomial case only. Defaults to "one" for less aggressive filtering
plot: Boolean whether to draw the misclassification error lambda plot for a cv.glmnet object. Defaults to FALSE
...: arguments to be passed to glmnet::cv.glmnet

Value

feature_calculations object containing a data frame of the reduced feature set

Author

Trent Henderson

Examples


library(theft)

features <- theft::calculate_features(theft::simData,
  feature_set = "catch22")
#> Running computations for catch22...

best_features <- shrink(features)