helper function to implement MCP penalty
The helper functions to implement each penalty.

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

MCP function

<dl><dt>z</dt>
<dd>a vector representing the solution over active set at each feature</dd>
<dt>l1</dt>
<dd>upper bound (on beta)</dd>
<dt>l2</dt>
<dd>lower bound (on beta)</dd>
<dt>gamma</dt>
<dd>The tuning parameter of the MCP penalty</dd>
<dt>v</dt>
<dd>the 'xtx' term</dd></dl>

Arguments

helper function to implement MCP penalty
The helper functions to implement each penalty. — MCP

<dl>

<dt>z</dt>
<dd>a vector representing the solution over active set at each feature</dd>


<dt>l1</dt>
<dd>upper bound (on beta)</dd>


<dt>l2</dt>
<dd>lower bound (on beta)</dd>


<dt>gamma</dt>
<dd>The tuning parameter of the MCP penalty</dd>


<dt>v</dt>
<dd>the 'xtx' term</dd>

</dl>

MCP: helper function to implement MCP penalty The helper functions to implement each penalty.

Description

Usage

Value

Arguments