untransform_in_memory

This function unwinds the initial standardization of the data to obtain
coefficient values on their original scale. It is called by plmm_format().

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

untransform_in_memory function

<dl><dt>std_scale_beta</dt>
<dd>The estimated coefficients on the standardized scale</dd>
<dt>p</dt>
<dd>The number of columns in the original design matrix</dd>
<dt>std_X_details</dt>
<dd>A list with 3 elements describing the standardized design matrix BEFORE rotation; this should have elements 'scale', 'center', and 'ns'</dd>
<dt>use_names</dt>
<dd>Logical: should names be added? Defaults to TRUE. Set to FALSE inside of <code>cvf()</code> helper, as 'ns' will vary within CV folds.</dd></dl>

Arguments

Untransform coefficient values back to the original scale In memory — untransform_in_memory

<dl>

<dt>std_scale_beta</dt>
<dd>The estimated coefficients on the standardized scale</dd>


<dt>p</dt>
<dd>The number of columns in the original design matrix</dd>


<dt>std_X_details</dt>
<dd>A list with 3 elements describing the standardized design matrix BEFORE rotation; this should have elements 'scale', 'center', and 'ns'</dd>


<dt>use_names</dt>
<dd>Logical: should names be added? Defaults to TRUE. Set to FALSE inside of <code>cvf()</code> helper, as 'ns' will vary within CV folds.</dd>

</dl>

untransform_in_memory: Untransform coefficient values back to the original scale In memory

Description

Usage

Value

Arguments