Partitioning R2 in GLMMs
Adds columns for CI
List to data.frame with bootstrap samples per row
Parametric bootstrapping
Calculates CI from bootstrap replicates
Get tidy summary of fixed effect estimates
Calculate fixed effect variance from a reduced model
Plot a partR2 object
Get beta weights
Get numerator dfs for reduced models
Extracts random effect variances
Extract variance components from merMod.
Create list of combination of variables.
Merge partR2 objects to combine R2s for main effects and interactions
Modify term names if partbatch is a named list
Adds an observational level random effect to a model, if applicable.
Calculate part R2
partR2: Partitioning R2 in generalized linear mixed models
Partitioning R2 (R-square) in mixed models
Pipe operator
Print a partR2 object
Calculate R2
Structure coefficients
sim_data dataset
Complete summary of a partR2 object
Get variance components for binomial model with binary response.
Get variance components for gaussian model.
Get variance components for merMod with poisson response.
Get variance components for binomial model with proportion response.
Captures and suppresses (still to find out why) warnings of an express...
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).