R2_pe

internal

Partitioning the R2 of GLMMs into variation explained by each
predictor and combination of predictors using semi-partial (part) R2 and
inclusive R2. Methods are based on the R2 for GLMMs described in
Nakagawa & Schielzeth (2013) and Nakagawa, Johnson & Schielzeth (2017).

Martin A Stoffel

partR2

Partitioning R2 in GLMMs

Martin A. Stoffel

Shinichi Nakagawa

Holger Schielzeth

R2_pe function

<dl><dt>mod</dt>
<dd>merMod object</dd>
<dt>expct</dt>
<dd>Expectation</dd>
<dt>overdisp_name</dt>
<dd>Name of overdispersion term</dd>
<dt>R2_type</dt>
<dd>"marginal" or "conditional"</dd></dl>

Arguments

Calculate R2 — R2_pe

<dl>

<dt>mod</dt>
<dd>merMod object</dd>


<dt>expct</dt>
<dd>Expectation</dd>


<dt>overdisp_name</dt>
<dd>Name of overdispersion term</dd>


<dt>R2_type</dt>
<dd>"marginal" or "conditional"</dd>

</dl>

Calculate R2

R2_pe: Calculate R2

Description

Usage

Value

Arguments