rsq.lmm | R Documentation |
Calculate the R-squared for linear mixed models.
rsq.lmm(fitObj,adj=FALSE)
fitObj |
an object of class "merMod" or "lmerMod" or "lme", usually, a result of a call to lmer in lme4, or lme in nlme. |
adj |
logical; if TRUE, calculate the adjusted R^2. |
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).
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. |
Dabao Zhang, Department of Statistics, Purdue University
Zhang, D. (2020). Coefficients of determination for mixed-effects models. arXiv:2007.0867.
rsq, rsq.v
.
# 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)
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