MultinomialExample

Randomly generated data for multinomial regression example.

data

Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression; see <doi:10.18637/jss.v033.i01> and <doi:10.18637/jss.v039.i05>. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (<doi:10.18637/jss.v106.i01>). This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers cited.

Trevor Hastie

glmnet

Lasso and Elastic-Net Regularized Generalized Linear Models

Jerome Friedman

Rob Tibshirani

Balasubramanian Narasimhan

Kenneth Tay

Noah Simon

Junyang Qian

James Yang

MultinomialExample function

List containing the following elements:<dl>
<dt>x</dt>
<dd>500 by 30 matrix of numeric values.</dd><dt>y</dt>
<dd>Numeric vector of length 500 containing 142 ones, 174 twos
and 184 threes.</dd>
</dl>

Format

Synthetic dataset with multinomial response — MultinomialExample

List containing the following elements:<dl>
<dt>x</dt>
<dd>500 by 30 matrix of numeric values.</dd>

<dt>y</dt>
<dd>Numeric vector of length 500 containing 142 ones, 174 twos
and 184 threes.</dd>


</dl>

Synthetic dataset with multinomial response

MultinomialExample: Synthetic dataset with multinomial response

Description

Usage

Arguments

Format