rsq.glmm: R-Squared for Generalized Linear Mixed Models

In rsq: R-Squared and Related Measures

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

vresidual, rsq, rsq.v.

Examples

require(lme4)
data(cbpp)
glmm1 <- glmer(cbind(incidence,size-incidence)~period+(1|herd),data=cbpp,family=binomial)
rsq.glmm(glmm1)
rsq(glmm1)

rsq documentation built on Oct. 22, 2023, 5:07 p.m.