count_constant_features

A helper function to count constant features

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

count_constant_features function

<dl><dt>fbm</dt>
<dd>A filebacked <code>big.matrix</code></dd>
<dt>outfile</dt>
<dd>String specifying name of log file</dd>
<dt>quiet</dt>
<dd>Logical: should console messages be silenced? Defaults to FALSE</dd></dl>

Arguments

A helper function to count constant features — count_constant_features

<dl>

<dt>fbm</dt>
<dd>A filebacked <code>big.matrix</code></dd>


<dt>outfile</dt>
<dd>String specifying name of log file</dd>


<dt>quiet</dt>
<dd>Logical: should console messages be silenced? Defaults to FALSE</dd>

</dl>

count_constant_features: A helper function to count constant features

Description

Usage

Value

Arguments