.extract

Extracts <code>cv.glmnet</code>-like object.

internal

Implements sparse regression with paired covariates (<doi:10.1007/s11634-019-00375-6>). The paired lasso is designed for settings where each covariate in one set forms a pair with a covariate in the other set (one-to-one correspondence). For the optional correlation shrinkage, install ashr (<https://github.com/stephens999/ashr>) and CorShrink (<https://github.com/kkdey/CorShrink>) from GitHub (see README).

Armin Rauschenberger

palasso

Sparse Regression with Paired Covariates

.extract function

<dl><dt>fit</dt>
<dd>matrix with one row for each sample
("gaussian", "binomial" and "poisson"),
or one row for each fold (only "cox"),
and one column for each <code>lambda</code>
(output from .fit)</dd>
<dt>lambda</dt>
<dd>lambda sequence:
vector of decreasing positive values</dd>
<dt>cvm</dt>
<dd>mean cross-validated loss:
vector of same length as <code>lambda</code>
(output from .loss)</dd>
<dt>type.measure</dt>
<dd>...
loss function:
character "deviance", "mse", "mae", "class", or "auc"</dd></dl>

Arguments

Extraction — .extract

<dl>

<dt>fit</dt>
<dd>matrix with one row for each sample
("gaussian", "binomial" and "poisson"),
or one row for each fold (only "cox"),
and one column for each <code>lambda</code>
(output from .fit)</dd>


<dt>lambda</dt>
<dd>lambda sequence:
vector of decreasing positive values</dd>


<dt>cvm</dt>
<dd>mean cross-validated loss:
vector of same length as <code>lambda</code>
(output from .loss)</dd>


<dt>type.measure</dt>
<dd>...
loss function:
character "deviance", "mse", "mae", "class", or "auc"</dd>

</dl>

.extract: Extraction

Description

Usage

Arguments

Examples