rsq.glmm {rsq} | R Documentation |
R-Squared for Generalized Linear Mixed Models
Description
Calculate the variance-function-based R-squared for generalized linear mixed models.
Usage
rsq.glmm(fitObj,adj=FALSE)
Arguments
fitObj |
an object of class "glmerMod", usually, a result of a call to glmer or glmer.nb in lme4. |
adj |
logical; if TRUE, calculate the adjusted R^2. |
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).
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. |
Author(s)
Dabao Zhang, Department of Statistics, Purdue University
References
Zhang, D. (2017). A coefficient of determination for generalized linear models. The American Statistician, 71(4): 310-316.
Zhang, D. (2020). Coefficients of determination for mixed-effects models. arXiv:2007.08675.
See Also
Examples
require(lme4)
data(cbpp)
glmm1 <- glmer(cbind(incidence,size-incidence)~period+(1|herd),data=cbpp,family=binomial)
rsq.glmm(glmm1)
rsq(glmm1)