estimate_eta

This function is called internally by <code>plmm()</code>

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

estimate_eta function

<dl><dt>n</dt>
<dd>The number of observations</dd>
<dt>s</dt>
<dd>The non-zero eigenvalues of K, the realized relationship matrix</dd>
<dt>U</dt>
<dd>The eigenvectors of K associated with s</dd>
<dt>y</dt>
<dd>Continuous outcome vector</dd>
<dt>incpt_flag</dt>
<dd>Logical: Does the model require fitting an intercept?</dd></dl>

Arguments

Estimate eta (to be used in rotating the data) — estimate_eta

<dl>

<dt>n</dt>
<dd>The number of observations</dd>


<dt>s</dt>
<dd>The non-zero eigenvalues of K, the realized relationship matrix</dd>


<dt>U</dt>
<dd>The eigenvectors of K associated with s</dd>


<dt>y</dt>
<dd>Continuous outcome vector</dd>


<dt>incpt_flag</dt>
<dd>Logical: Does the model require fitting an intercept?</dd>

</dl>

estimate_eta: Estimate eta (to be used in rotating the data)

Description

Usage

Value

Arguments