weighted_mean_sd

Helper function to compute weighted mean and standard deviation.
Deals gracefully whether x is sparse matrix or not.

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

weighted_mean_sd function

<dl><dt>x</dt>
<dd>Observation matrix.</dd>
<dt>weights</dt>
<dd>Optional weight vector.</dd></dl>

Arguments

Helper function to compute weighted mean and standard deviation — weighted_mean_sd

<dl>

<dt>x</dt>
<dd>Observation matrix.</dd>


<dt>weights</dt>
<dd>Optional weight vector.</dd>

</dl>

weighted_mean_sd: Helper function to compute weighted mean and standard deviation

Description

Usage

Value

Arguments