| Type: | Package |
| Title: | Hierarchical Partitioning of Marginal R2 for Generalized Mixed-Effect Models |
| Version: | 0.1-8 |
| Date: | 2025-03-23 |
| Depends: | R (≥ 3.4.0),MuMIn,ggplot2,vegan |
| Imports: | lme4 |
| Maintainer: | Jiangshan Lai <lai@njfu.edu.cn> |
| Description: | Conducts hierarchical partitioning to calculate individual contributions of each predictor (fixed effects) towards marginal R2 for generalized linear mixed-effect model (including lm, glm and glmm) based on output of r.squaredGLMM() in 'MuMIn', applying the algorithm of Lai J.,Zou Y., Zhang S.,Zhang X.,Mao L.(2022)glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models.Journal of Plant Ecology,15(6)1302-1307<doi:10.1093/jpe/rtac096>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| Encoding: | UTF-8 |
| URL: | https://github.com/laijiangshan/glmm.hp |
| RoxygenNote: | 7.3.1 |
| NeedsCompilation: | no |
| Packaged: | 2025-03-24 10:08:51 UTC; lai |
| Author: | Jiangshan Lai 
[aut, cre],
Kim Nimon [aut],
Yao Liu [aut] |
| Repository: | CRAN |
| Date/Publication: | 2025-03-24 10:30:02 UTC |
Internal function for glmm.hp() to create diagonal matrix
Description
Internal function for glmm.hp() to create diagonal matrix
Usage
creatbin(col, binmatrix)
Arguments
col |
Imput number. |
binmatrix |
Imput empty matrix. |
Value
a matrix
a matix |
A diagonal matrix |
Internal function for glmm.hp()
Description
Internal function for glmm.hp()
Usage
genList(ivlist, value)
Arguments
ivlist |
The names of explanatory variable. |
value |
The sequence ID. |
Value
a vector
newlist |
A vector for variable index. |
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
Lai J.,Zhu W., Cui D.,Mao L.(2023)Extension of the glmm.hp package to Zero-Inflated generalized linear mixed models and multiple regression.Journal of Plant Ecology,16(6):rtad038<DOI:10.1093/jpe/rtad038>
Lai J.,Zou Y., Zhang S.,Zhang X.,Mao L.(2022)glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models.Journal of Plant Ecology,15(6):1302-1307<DOI:10.1093/jpe/rtac096>
Lai J.,Zou Y., Zhang J.,Peres-Neto P.(2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution,13(4):782-788<DOI:10.1111/2041-210X.13800>
Chevan, A. & Sutherland, M. (1991). Hierarchical partitioning. American Statistician, 45, 90-96. doi:10.1080/00031305.1991.10475776
Nimon, K., Oswald, F.L. & Roberts, J.K. (2013). Yhat: Interpreting regression effects. R package version 2.0.0.
Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133-142.
Nakagawa, S., Johnson, P. C., & Schielzeth, H. (2017). The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of the Royal Society Interface, 14(134), 20170213.
Ezekiel, M. (1930) Methods of Correlational Analysis. Wiley, New York.
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)
Internal function for glmm.hp() to determine whether the odd number
Description
Internal function for glmm.hp() to determine whether the odd number
Usage
odd(val)
Arguments
val |
Imput number. |
Value
a logical value
Logical value |
TRUE or FALSE |
Plot for a glmm.hp object
Description
Plot for a glmm.hp object
Usage
## S3 method for class 'glmmhp'
plot(x, plot.perc = FALSE, color = NULL, n = 1, dig = 4, ...)
Arguments
x |
A |
plot.perc |
Logical;if TRUE, the bar plot (based on ggplot2 package) of the percentage to individual effects of variables or groups towards total explained variation, the default is FALSE to show plot with original individual effects. |
color |
Color of variables. |
n |
Integer; which marginal R2 in output of r.squaredGLMM to plot. |
dig |
Integer; number of decimal places in Venn diagram. |
... |
unused |
Value
a ggplot object
Author(s)
Jiangshan Lai lai@njfu.edu.cn
Examples
library(MuMIn)
library(lme4)
mod1 <- lmer(Sepal.Length ~ Petal.Length + Petal.Width +(1 | Species), data = iris)
a <- glmm.hp(mod1)
plot(a)
mod3 <- lm(Sepal.Length ~ Petal.Length+Petal.Width,data = iris)
plot(glmm.hp(mod3,type="R2"))
plot(glmm.hp(mod3,commonality=TRUE),color = c("#8DD3C7", "#FFFFB3"))