s_soda_model

The design matrix, of dimensions n * p, without an intercept. Each row is an observation vector.

The response vector of dimension n * 1.

S-SODA assumes within each slice the X vector follow multivariate normal distribution. This function estimates the mean vector and covariance matrix of X for each slice.

 S-SODA 

Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under logistic regression and quadratic discriminant analysis. We also provide an extension, S-SODA, for dealing with the variable selection problem for semi-parametric models with continuous responses.

s_soda_model: S-SODA model estimation.

Description

Usage

Arguments

Value