get_eta

Given x, coefficients and intercept, return linear predictions. Wrapper that
works with both regular and sparse x. Only works for single set of coefficients
and intercept.

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

get_eta function

<dl><dt>x</dt>
<dd>Input matrix, of dimension <code>nobs x nvars</code>; each row is an
observation vector. If it is a sparse matrix, it is assumed to be unstandardized.
It should have attributes <code>xm</code> and <code>xs</code>, where <code>xm(j)</code> and
<code>xs(j)</code> are the centering and scaling factors for variable j respsectively.
If it is not a sparse matrix, it is assumed to be standardized.</dd>
<dt>beta</dt>
<dd>Feature coefficients.</dd>
<dt>a0</dt>
<dd>Intercept.</dd></dl>

Arguments

Helper function to get etas (linear predictions) — get_eta

<dl>

<dt>x</dt>
<dd>Input matrix, of dimension <code>nobs x nvars</code>; each row is an
observation vector. If it is a sparse matrix, it is assumed to be unstandardized.
It should have attributes <code>xm</code> and <code>xs</code>, where <code>xm(j)</code> and
<code>xs(j)</code> are the centering and scaling factors for variable j respsectively.
If it is not a sparse matrix, it is assumed to be standardized.</dd>


<dt>beta</dt>
<dd>Feature coefficients.</dd>


<dt>a0</dt>
<dd>Intercept.</dd>

</dl>

get_eta: Helper function to get etas (linear predictions)

Description

Usage

Arguments