compute_blup

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

compute_blup function

<dl><dt>fit</dt>
<dd>An object returned by <code>plmm()</code></dd>
<dt>Xb</dt>
<dd>Linear predictor</dd>
<dt>Sigma_21</dt>
<dd>Covariance matrix between the training and the testing data. Extracted from <code>estimated_Sigma</code> that is generated using all observations</dd>
<dt>idx</dt>
<dd>Vector of indices of the penalty parameter <code>lambda</code> at which predictions are required. By default, all indices are returned.</dd></dl>

Arguments

A function to compute the BLUP — compute_blup

<dl>

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


<dt>Xb</dt>
<dd>Linear predictor</dd>


<dt>Sigma_21</dt>
<dd>Covariance matrix between the training and the testing data. Extracted from <code>estimated_Sigma</code> that is generated using all observations</dd>


<dt>idx</dt>
<dd>Vector of indices of the penalty parameter <code>lambda</code> at which predictions are required. By default, all indices are returned.</dd>

</dl>

A function to compute the BLUP

compute_blup: A function to compute the BLUP

Description

Usage

Value

Arguments