In mastoffel/partR2: Partitioning R2 in GLMMs

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)

#library("txtplot")
library("badger")

partR2

The goal of partR2 is to estimate R^2^ in GLMMs (sensu Nakagawa & Schielzeth 2013) and to partition the R^2^ into the variance explained by the predictors.

The package takes a fitted lme4 model as input and gives you:

• R^2^ (marginal or conditional)
• Part (semi-partial) R^2^, the variance explained uniquely by each predictor and combinations of predictors
• Inclusive R^2^, the variance explained by a predictor independent of all other predictors
• Structure coefficients, the correlation between a predictor and the fitted response
• Beta weights, standardised model estimates

All estimates can be combined with parametric bootstrapping to get confidence intervals.

Installation

You can install the stable version of partR2 from CRAN with:

install.packages("partR2")

Or the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("mastoffel/partR2", build_vignettes = TRUE, dependencies = TRUE) 

Access the vignette with:

# check vignette
browseVignettes("partR2")

partR2 is still in an early phase of development and might contain bugs. If you find one, please report a minimal reproducible example in the issues.

Citation

When using partR2, please cite our paper:

Stoffel MA, Nakagawa S, Schielzeth H. 2021. partR2: partitioning R2 in generalized linear mixed models. PeerJ 9:e11414 https://doi.org/10.7717/peerj.11414

Example

library(partR2)
library(lme4)

?`partR2-package`

# load data
data(biomass)
# fit lme4 model
mod <- lmer(Biomass ~  Year + Temperature + SpeciesDiversity + (1|Population),
            data = biomass)
# R2s and partial R2s
(R2 <- partR2(mod,  partvars = c("SpeciesDiversity", "Temperature", "Year"),
              R2_type = "marginal", nboot = 100, CI = 0.95))

And to plot the results:

forestplot(R2, type = "R2", line_size = 0.7, text_size = 14, point_size = 3)

mastoffel/partR2 documentation built on March 9, 2024, 1:16 p.m.