arguments

This page lists the arguments for the (internal) "palasso" function(s).

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

arguments function

<dl><dt>y</dt>
<dd>response:
vector of length \(n\)</dd>
<dt>X</dt>
<dd>covariates:
list of matrices,
each with \(n\) rows (samples)
and \(p\) columns (variables)</dd>
<dt>max</dt>
<dd>maximum number of non-zero coefficients:
positive numeric, or <code>NULL</code> (no sparsity constraint)</dd>
<dt>...</dt>
<dd>further arguments for <code>cv.glmnet</code> or
<code>glmnet</code></dd>
<dt>x</dt>
<dd>covariates:
matrix with \(n\) rows (samples)
and \(k * p\) columns (variables)</dd>
<dt>args</dt>
<dd>options for paired lasso:
list of arguments
(output from .dims and .args)</dd>
<dt>nfolds</dt>
<dd>number of folds:
positive integer
(\(&gt;= 10\) recommended)</dd>
<dt>foldid</dt>
<dd>fold identifiers:
vector of length \(n\),
with entries from \(1\) to <code>nfolds</code></dd>
<dt>cor</dt>
<dd>correlation coefficients:
list of \(k\) vectors of length \(p\)
(one vector for each covariate set with
one entry for each covariate)</dd>
<dt>lambda</dt>
<dd>lambda sequence:
vector of decreasing positive values</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>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>cvm</dt>
<dd>mean cross-validated loss:
vector of same length as <code>lambda</code>
(output from .loss)</dd></dl>

Arguments

Arguments for "palasso" — arguments

<dl>

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


<dt>X</dt>
<dd>covariates:
list of matrices,
each with \(n\) rows (samples)
and \(p\) columns (variables)</dd>


<dt>max</dt>
<dd>maximum number of non-zero coefficients:
positive numeric, or <code>NULL</code> (no sparsity constraint)</dd>


<dt>...</dt>
<dd>further arguments for <code>cv.glmnet</code> or
<code>glmnet</code></dd>


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


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


<dt>nfolds</dt>
<dd>number of folds:
positive integer
(\(&gt;= 10\) recommended)</dd>


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


<dt>cor</dt>
<dd>correlation coefficients:
list of \(k\) vectors of length \(p\)
(one vector for each covariate set with
one entry for each covariate)</dd>


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

</dl>

arguments: Arguments for "palasso"

Description

Arguments