glmm.hp {glmm.hp}R Documentation

Hierarchical Partitioning of Marginal R2 for Generalized Mixed-Effect Models

Description

Hierarchical Partitioning of Marginal R2 for Generalized Mixed-Effect Models

Usage

glmm.hp(mod, iv = NULL, type = "adjR2", commonality = FALSE)

Arguments

mod

Fitted lme4,nlme,glmmTMB,glm or lm model objects.

iv

optional The relative importance of predictor groups will be assessed. The input for iv should be a list, where each element contains the names of variables belonging to a specific group. These variable names must correspond to the predictor variables defined in the model (mod).

type

The type of R-square of lm, either "R2" or "adjR2", in which "R2" is unadjusted R-square and "adjR2" is adjusted R-square, the default is "adjR2". The adjusted R-square is calculated using Ezekiel's formula (Ezekiel 1930) for lm.

commonality

Logical; If TRUE, the result of commonality analysis (2^N-1 fractions for N predictors) is shown, the default is FALSE.

Details

This function conducts hierarchical partitioning to calculate the individual contributions of each predictor towards total (marginal) R2 for Generalized Linear Mixed-effect Model (including lm,glm and glmm). The marginal R2 is the output of r.squaredGLMM in MuMIn package for glm and glmm.

Value

r.squaredGLMM

The R2 for the full model.

commonality.analysis

If commonality=TRUE, a matrix containing the value and percentage of all commonality (2^N-1 for N predictors or matrices).

hierarchical.partitioning

A matrix containing individual effects and percentage of individual effects towards total (marginal) R2 for each predictor.

Author(s)

Jiangshan Lai lai@njfu.edu.cn

References

Examples

library(MuMIn)
library(lme4)
mod1 <- lmer(Sepal.Length ~ Petal.Length + Petal.Width+(1|Species),data = iris)
r.squaredGLMM(mod1)
glmm.hp(mod1)
a <- glmm.hp(mod1)
plot(a)
mod2 <- glm(Sepal.Length ~ Petal.Length + Petal.Width, data = iris)
r.squaredGLMM(mod2)
glmm.hp(mod2)
b <- glmm.hp(mod2)
plot(b)
plot(glmm.hp(mod2))
mod3 <- lm(Sepal.Length ~ Petal.Length + Petal.Width + Petal.Length:Petal.Width, data = iris)
glmm.hp(mod3,type="R2")
glmm.hp(mod3,commonality=TRUE)
mod4 <- lm(Sepal.Length ~ Petal.Length + Petal.Width + Sepal.Width, data = iris)
iv=list(pred1="Sepal.Width",pred2=c("Petal.Length","Petal.Width"))
glmm.hp(mod4,iv)

[Package glmm.hp version 0.1-8 Index]