README.md

In partR2: Partitioning R2 in GLMMs

partR2

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.

partR2 is still in an early phase of development and might contain bugs.

Installation

You can install the development version of partR2 from GitHub with:

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

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))
#> 
#> 
#> R2 (marginal) and 95% CI for the full model: 
#> # A tibble: 1 x 5
#>      R2 CI_lower CI_upper nboot   ndf
#>   <dbl>    <dbl>    <dbl> <int> <dbl>
#> 1 0.513    0.445    0.594   100     4
#> 
#> ----------
#> 
#> Part (semi-partial) R2:
#> # A tibble: 8 x 6
#>   `Predictor(s)`                       R2 CI_lower CI_upper nboot   ndf
#>   <chr>                             <dbl>    <dbl>    <dbl> <int> <dbl>
#> 1 Model                             0.513   0.445     0.594   100     4
#> 2 SpeciesDiversity                  0.173   0.0762    0.282   100     3
#> 3 Temperature                       0.306   0.221     0.398   100     3
#> 4 Year                              0.014   0         0.145   100     3
#> 5 SpeciesDiversity+Temperature      0.492   0.423     0.571   100     2
#> 6 SpeciesDiversity+Year             0.186   0.0905    0.293   100     2
#> 7 Temperature+Year                  0.328   0.246     0.417   100     2
#> 8 SpeciesDiversity+Temperature+Year 0.513   0.445     0.594   100     1

And to plot the results:

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

Citation

When using partR2, please cite our preprint for now, and look out for the peer-reviewed paper, which will hopefully come out soon.

Stoffel, MA, Nakagawa, S, & Schielzeth, H (2020). partR2: Partitioning R2 in generalized linear mixed models. bioRxiv.

partR2 documentation built on Jan. 18, 2021, 5:06 p.m.