Package ‘MuMIn’

46 r.squaredGLMM
## End(Not run)
r.squaredGLMM
Pseudo-R-squared for Generalized Mixed-Effect models
Description
Usage
Calculate conditional and marginal coefficient of determination for Generalized mixed-effect models
(R 2 GLMM ).
r.squaredGLMM(x)
Arguments
x
a fitted linear model object.
Details
For mixed-effects models, R 2 can be categorized into two types. Marginal RGLMM 2 represents the
variance explained by fixed factors, and is defined as:
Value
Note
Conditional RGLMM 2 is interpreted as variance explained by both fixed and random factors (i.e.
the entire model), and is calculated according to the equation:
R 2 GLMM(c) =
σ 2 f + ∑ u
l=1 σ2 l
σ 2 f + ∑ u
l=1 σ2 l + σ2 e + σ 2 d
where σf 2 is the variance of the fixed effect components, and ∑ σl
2 is the sum of all u variance
components (group, individual, etc.), σl
2 is the variance due to additive dispersion and σd 2 is the
distribution-specific variance.
r.squaredGLMM returns a numeric vector with two values for marginal and conditional R 2 GLMM .
RGLMM 2 can be calculated also for fixed-effect models. In the simpliest case of OLS it reduces to
var(fitted) / (var(fitted) + deviance / 2). Unlike likelihood-ratio based R 2 for OLS,
value of this statistic differs from that of the classical R 2 .
Currently methods exist for classes: mer(Mod), lme, glmmML and (g)lm.
See note in r.squaredLR help page for comment on using R 2 in model selection.