construct_variance

a function to create the estimated variance matrix from a PLMM fit

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

construct_variance function

<dl><dt>fit</dt>
<dd>An object returned by <code>plmm()</code></dd>
<dt>K</dt>
<dd>An optional matrix</dd>
<dt>eta</dt>
<dd>An optional numeric value between 0 and 1; if <code>fit</code> is not supplied, then this option must be specified.</dd></dl>

Arguments

a function to create the estimated variance matrix from a PLMM fit — construct_variance

<dl>

<dt>fit</dt>
<dd>An object returned by <code>plmm()</code></dd>


<dt>K</dt>
<dd>An optional matrix</dd>


<dt>eta</dt>
<dd>An optional numeric value between 0 and 1; if <code>fit</code> is not supplied, then this option must be specified.</dd>

</dl>

construct_variance: a function to create the estimated variance matrix from a PLMM fit

Description

Usage

Value

Arguments