part_R2s

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

part_R2s 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>
<dt>all_comb</dt>
<dd>variable combinations to remove to calculate R2</dd>
<dt>partition</dt>
<dd>check if R2 partitioning is needed</dd>
<dt>data_mod</dt>
<dd>data.frame</dd>
<dt>allow_neg_r2</dt>
<dd>TRUE or FALSE</dd></dl>

Arguments

Calculate part R2 — part_R2s

<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>


<dt>all_comb</dt>
<dd>variable combinations to remove to calculate R2</dd>


<dt>partition</dt>
<dd>check if R2 partitioning is needed</dd>


<dt>data_mod</dt>
<dd>data.frame</dd>


<dt>allow_neg_r2</dt>
<dd>TRUE or FALSE</dd>

</dl>

Calculate part R2

part_R2s: Calculate part R2

Description

Usage

Value

Arguments