coef.logit

An object with S3 class <code>"logit"</code>

object

The indices of the regularizaiton parameters in the solution path to be displayed. The default values are <code>c(1:3)</code>.

lambda.idx

The indices of the estimate regression coefficient vectors in the solution path to be displayed. The default values are <code>c(1:3)</code>.

beta.idx

Extract estimated regression coefficient vectors from the solution path.

Implement the pathwise calibrated sparse shooting algorithm (PICASSO) for high dimensional regularized sparse linear regression, sparse logistic regression, and sparse undirected graphical model estimation. As a pathwise coordinate optimization algorithm, PICASSO exploits warm start initialization, active set updating, and strong rule for coordinate preselection to boost computation, and attains a linear convergence to a unique sparse local optimum with optimal statistical properties. Moreover, users can choose different regularizers, including L1, MCP, and SCAD regularizers. The computation is memory-optimized using the sparse matrix output.

coef.logit: Extract Model Coefficients for an object with S3 class `"logit"`

Description

Usage

Arguments

See Also