Internal function for <code>cv_plmm()</code> which calls <code>plmm()</code> on a fold subset of the original data.

internal

Fits penalized linear mixed models that correct for
unobserved confounding factors. 'plmmr' infers and corrects for the
presence of unobserved confounding effects such as population
stratification and environmental heterogeneity. It then fits a linear
model via penalized maximum likelihood. Originally designed for the
multivariate analysis of single nucleotide polymorphisms (SNPs)
measured in a genome-wide association study (GWAS), 'plmmr' eliminates
the need for subpopulation-specific analyses and post-analysis p-value
adjustments. Functions for the appropriate processing of 'PLINK'
files are also supplied. For examples, see the package homepage
<https://pbreheny.github.io/plmmr/>.

Patrick Breheny

plmmr

Penalized Linear Mixed Models for Correlated Data

Tabitha K. Peter

Anna C. Reisetter

Yujing Lu

Oscar A. Rysavy

Patrick J. Breheny

cvf function

<dl><dt>i</dt>
<dd>Fold number to be excluded from fit.</dd>
<dt>fold</dt>
<dd>n-length vector of fold-assignments.</dd>
<dt>type</dt>
<dd>A character argument indicating what should be returned from <code>predict.plmm()</code>. If <code>type = 'lp'</code> predictions are based on the linear predictor, \(X \beta\).
If <code>type = 'blup'</code>, predictions are based on the linear predictor plus the estimated random effect (BLUP).</dd>
<dt>cv_args</dt>
<dd>List of additional arguments to be passed to plmm.</dd>
<dt>...</dt>
<dd>Optional arguments to <code>predict_within_cv()</code></dd></dl>

Arguments

Cross-validation internal function for cv_plmm() — cvf

<dl>

<dt>i</dt>
<dd>Fold number to be excluded from fit.</dd>


<dt>fold</dt>
<dd>n-length vector of fold-assignments.</dd>


<dt>type</dt>
<dd>A character argument indicating what should be returned from <code>predict.plmm()</code>. If <code>type = 'lp'</code> predictions are based on the linear predictor, \(X \beta\).
If <code>type = 'blup'</code>, predictions are based on the linear predictor plus the estimated random effect (BLUP).</dd>


<dt>cv_args</dt>
<dd>List of additional arguments to be passed to plmm.</dd>


<dt>...</dt>
<dd>Optional arguments to <code>predict_within_cv()</code></dd>

</dl>

cvf: Cross-validation internal function for `cv_plmm()`

Description

Usage

Value

Arguments