oscar.binarize: Binary logical indicator matrix representation of an oscar...

In oscar: Optimal Subset Cardinality Regression (OSCAR) Models Using the L0-Pseudonorm

Binary logical indicator matrix representation of an oscar object's coefficients (zero vs. non-zero, i.e. feature inclusion)

Description

Create a sparse matrix with binary indicator 1 indicating that a coefficient was non-zero, and value 0 (or . in sparse matrix) indicating that a coefficient was zero (i.e. feature not included)

Usage

oscar.binarize(fit, kmax = fit@kmax)

Arguments

fit	Fit oscar-model object
kmax	Create matrix until kmax-value; by default same as for fit object, but for high dimensional tasks one may wish to reduce this

Details

The matrix consists of TRUE/FALSE values, and is very similar to the oscar.sparsify, where the function provides estimate values in a sparse matrix format.

Value

A binary logical indicator matrix of variables (rows) as a function of cardinality k (columns), where elements are binary indicators for 1 as non-zero and 0 as zero.

Examples

if(interactive()){
  data(ex)
  fit <- oscar(x=ex_X, y=ex_Y, k=ex_K, w=ex_c, family='cox')
  oscar.binarize(fit, kmax=5)
}

oscar documentation built on Oct. 2, 2023, 5:08 p.m.