coef.cv_plmm

Coef method for <code>cv_plmm</code> class

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

coef.cv_plmm function

<dl><dt>object</dt>
<dd>An object of class <code>cv_plmm</code>.</dd>
<dt>lambda</dt>
<dd>A numeric vector of lambda values.</dd>
<dt>which</dt>
<dd>Vector of lambda indices for which to return coefficients.
Defaults to lambda index with minimum CVE.</dd>
<dt>...</dt>
<dd>Additional arguments (not used).</dd></dl>

Arguments

Coef method for cv_plmm class — coef.cv_plmm

<dl>

<dt>object</dt>
<dd>An object of class <code>cv_plmm</code>.</dd>


<dt>lambda</dt>
<dd>A numeric vector of lambda values.</dd>


<dt>which</dt>
<dd>Vector of lambda indices for which to return coefficients.
Defaults to lambda index with minimum CVE.</dd>


<dt>...</dt>
<dd>Additional arguments (not used).</dd>

</dl>

coef.cv_plmm: Coef method for `cv_plmm` class

Description

Usage

Value

Arguments

Examples