align_ids

A helper function to support <code>process_plink()</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

align_ids function

<dl><dt>id_var</dt>
<dd>String specifying the variable name of the ID column</dd>
<dt>quiet</dt>
<dd>Logical: should a message be printed?</dd>
<dt>add_predictor</dt>
<dd>External data to include in design matrix. This is the add_predictors... arg in <code>process_plink()</code></dd>
<dt>og_ids</dt>
<dd>Character vector with the PLINK ids (FID or IID) from the original data (i.e., the data before any subsetting from handling missing phenotypes)</dd></dl>

Arguments

A helper function to support process_plink() — align_ids

<dl>

<dt>id_var</dt>
<dd>String specifying the variable name of the ID column</dd>


<dt>quiet</dt>
<dd>Logical: should a message be printed?</dd>


<dt>add_predictor</dt>
<dd>External data to include in design matrix. This is the add_predictors... arg in <code>process_plink()</code></dd>


<dt>og_ids</dt>
<dd>Character vector with the PLINK ids (FID or IID) from the original data (i.e., the data before any subsetting from handling missing phenotypes)</dd>

</dl>

align_ids: A helper function to support `process_plink()`

Description

Usage

Value

Arguments