log_lik

This function allows you to evaluate the negative log-likelihood of a linear mixed model under the assumption of a null model in order to estimate the variance parameter, eta.

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

log_lik function

<dl><dt>eta</dt>
<dd>The proportion of variance in the outcome that is attributable to causal SNP effects. In other words, signal-to-noise ratio.</dd>
<dt>n</dt>
<dd>The number of observations</dd>
<dt>s</dt>
<dd>The singular values of K, the realized relationship matrix</dd>
<dt>U</dt>
<dd>The left-singular vectors of the standardized design matrix</dd>
<dt>y</dt>
<dd>Continuous outcome vector.</dd>
<dt>rot_y</dt>
<dd>Optional: if y has already been rotated, then this can be supplied.</dd></dl>

Arguments

Evaluate the negative log-likelihood of an intercept-only Gaussian plmm model — log_lik

<dl>

<dt>eta</dt>
<dd>The proportion of variance in the outcome that is attributable to causal SNP effects. In other words, signal-to-noise ratio.</dd>


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


<dt>s</dt>
<dd>The singular values of K, the realized relationship matrix</dd>


<dt>U</dt>
<dd>The left-singular vectors of the standardized design matrix</dd>


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


<dt>rot_y</dt>
<dd>Optional: if y has already been rotated, then this can be supplied.</dd>

</dl>

log_lik: Evaluate the negative log-likelihood of an intercept-only Gaussian plmm model

Description

Usage

Value

Arguments