.loss

Calculates mean cross-validated loss

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

.loss function

<dl><dt>y</dt>
<dd>response:
vector of length \(n\)</dd>
<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>family</dt>
<dd>model family:
character "gaussian", "binomial", "poisson", or "cox"</dd>
<dt>type.measure</dt>
<dd>...
loss function:
character "deviance", "mse", "mae", "class", or "auc"</dd>
<dt>foldid</dt>
<dd>fold identifiers:
vector of length \(n\),
with entries from \(1\) to <code>nfolds</code></dd></dl>

Arguments

Cross-validation loss — .loss

<dl>

<dt>y</dt>
<dd>response:
vector of length \(n\)</dd>


<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>family</dt>
<dd>model family:
character "gaussian", "binomial", "poisson", or "cox"</dd>


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


<dt>foldid</dt>
<dd>fold identifiers:
vector of length \(n\),
with entries from \(1\) to <code>nfolds</code></dd>

</dl>

.loss: Cross-validation loss

Description

Usage

Value

Arguments

Examples