Repeatedly leaves out samples, and predicts their response.

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

.cv function

<dl><dt>y</dt>
<dd>response:
vector of length \(n\)</dd>
<dt>x</dt>
<dd>covariates:
matrix with \(n\) rows (samples)
and \(k * p\) columns (variables)</dd>
<dt>foldid</dt>
<dd>fold identifiers:
vector of length \(n\),
with entries from \(1\) to <code>nfolds</code></dd>
<dt>lambda</dt>
<dd>lambda sequence:
vector of decreasing positive values</dd>
<dt>args</dt>
<dd>options for paired lasso:
list of arguments
(output from .dims and .args)</dd></dl>

Arguments

Cross-validation — .cv

<dl>

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


<dt>x</dt>
<dd>covariates:
matrix with \(n\) rows (samples)
and \(k * p\) columns (variables)</dd>


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


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


<dt>args</dt>
<dd>options for paired lasso:
list of arguments
(output from .dims and .args)</dd>

</dl>

.cv: Cross-validation

Description

Usage

Value

Arguments

Examples