The goal of partR2 is to estimate R2 in GLMMs (sensu
Nakagawa & Schielzeth 2013) and to partition the R2 into the
variance explained by the predictors.
The package takes a fitted lme4 model as input and gives you:
- R2 (marginal or conditional)
- Part (semi-partial) R2, the variance explained uniquely by each predictor and combinations of predictors
- Inclusive R2, 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.
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.
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
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))
#>
#>
#> R2 (marginal) and 95% CI for the full model:
#> R2 CI_lower CI_upper nboot ndf
#> 0.5133 0.4383 0.5848 100 4
#>
#> ----------
#>
#> Part (semi-partial) R2:
#> Predictor(s) R2 CI_lower CI_upper nboot ndf
#> Model 0.5133 0.4383 0.5848 100 4
#> SpeciesDiversity 0.1729 0.0682 0.2721 100 3
#> Temperature 0.3058 0.2128 0.3917 100 3
#> Year 0.0140 0.0000 0.1354 100 3
#> SpeciesDiversity+Temperature 0.4916 0.4147 0.5638 100 2
#> SpeciesDiversity+Year 0.1862 0.0827 0.2835 100 2
#> Temperature+Year 0.3276 0.2364 0.4117 100 2
#> SpeciesDiversity+Temperature+Year 0.5133 0.4383 0.5848 100 1And to plot the results:
forestplot(R2, type = "R2", line_size = 0.7, text_size = 14, point_size = 3)