rsq (version 2.7)

rsq.lmm: R-Squared for Linear Mixed Models

Description

Calculate the R-squared for linear mixed models.

Usage

rsq.lmm(fitObj,adj=FALSE)

Value

R_M^2

proportion of variation explained by the model in total, including both fixed-effects and random-efffects factors.

R_F^2

proportion of variation explained by the fixed-effects factors.

R_R^2

proportion of variation explained by the random-effects factors.

Arguments

fitObj

an object of class "merMod" or "lmerMod" or "lme", usually, a result of a call to lmer, or lme.

adj

logical; if TRUE, calculate the adjusted R^2.

Author

Dabao Zhang, Department of Epidemiology and Biostatistics, University of California, Irvine

Details

There are three types of R^2 calculated on the basis of observed response values, estimates of fixed effects, and variance components, i.e., model-based R_M^2 (proportion of variation explained by the model in total, including both fixed-effects and random-efffects factors), fixed-effects R_F^2 (proportion of variation explained by the fixed-effects factors), and random-effects R_R^2 (proportion of variation explained by the random-effects factors).

References

Zhang, D. (2022). Coefficients of determination for mixed-effects models. Journal of Agricultural, Biological and Environmental Statistics, 27: 674-689.

See Also

rsq, rsq.v.

Examples

# lmer in lme4
require(lme4)
lmm1 <- lmer(Reaction~Days+(Days|Subject),data=sleepstudy)
rsq(lmm1)
rsq.lmm(lmm1)

# lme in nlme
require(nlme)
lmm2 <- lme(Reaction~Days,data=sleepstudy,random=~Days|Subject)
rsq(lmm2)
rsq.lmm(lmm2)