rsqmlm: Calculates R-squared from lmer models

In lj5yn/mlmtools: Multi-Level Model Assessment Kit

Calculates R-squared from lmer models

Description

Calculates variance explained by lme4-fitted mixed-effects models.

Usage

rsqmlm(model, by_cluster = FALSE)

Arguments

model	A linear mixed-effects model of class lmerMod or lmerModLmerTest
by_cluster	Logical, if TRUE returns variance explained at each level

Value

Computes the percent variance explained by the model.

References

Nakagawa, S., Johnson, P. C., & Schielzeth, H. (2017). The coefficient of determination R 2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of the Royal Society Interface, 14(134), 20170213.

Examples

# Gaussian
## Read in data
data(instruction)
## Center mathkind by classid
center(dataset = instruction, x = "mathkind", grouping = "classid")
## Create model
mod <- lme4::lmer(mathgain ~ classid_mathkind.cmn +
classid_mathkind.devcmn + (1 | classid), data = instruction)
## Calculate r-squared
### To repress output: use invisible()
rsq <- rsqmlm(mod)
rsq
rsq$marginal
rsq$conditional

# Logistic
## Read in data
data(reporting)
## Create model
mod <- lme4::glmer(mention.outliers ~ Basics + (1 | Journal), data = reporting, family = "binomial")
## Calculate r-squared
### To repress output: use invisible()
rsq <- rsqmlm(mod)
rsq
rsq$marginal
rsq$conditional

lj5yn/mlmtools documentation built on March 10, 2023, 8:37 p.m.