phyloglm.hp: Hierarchical Partitioning of R2 for Phylogenetic Generalized...

In phylolm.hp: Hierarchical Partitioning of R2 for Phylogenetic Linear Regression

Hierarchical Partitioning of R2 for Phylogenetic Generalized Linear Regression

Description

Hierarchical Partitioning of R2 for Phylogenetic Generalized Linear Regression

Usage

phyloglm.hp(mod, iv = NULL, commonality = FALSE)

Arguments

mod	Fitted phylolm or phyloglm 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).
commonality	Logical; If TRUE, the result of commonality analysis is shown, the default is FALSE.

Details

This function conducts hierarchical partitioning to calculate the individual contributions of phylogenetic signal and each predictor towards total R2 from rr2 package for phylogenetic linear regression.

Value

Total.R2	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).
Individual.R2	A matrix containing individual effects and percentage of individual effects for phylogenetic tree and each predictor

Author(s)

Jiangshan Lai [e-mail]

References

• Lai J., He Y., Hou M,Zhang A.,Wang G., Mao L.,(2025) Evaluating the relative importance of phylogeny and predictors in Phylogenetic Generalized Linear Models using the phylolm.hp R package, Plant Diversity, 47(5):709-717>

• 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.

• Nimon, Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

Examples

library(phylolm)
library(rr2)
set.seed(231)
tre <- rcoal(60)
taxa <- sort(tre$tip.label) 
b0 <- 0      
b1 <- 0.3    
b2 <- 0.5 
b3 <- 0.4
x <- rTrait(n=1, phy=tre, model="lambda", parameters=list(ancestral.state=0, sigma2=15, lambda=0.9))          
x2 <- rTrait(n=1, phy=tre, model="lambda",  
parameters=list(ancestral.state=0, sigma2=10, lambda=0.9))  
x3 <- rTrait(n=1, phy=tre, model="lambda",  
parameters=list(ancestral.state=0, sigma2=13, lambda=0.9)) 
y <- b0 + b1 * x + b2 * x2 + b3*x3+ rTrait(n=1, phy=tre, model="lambda",
parameters=list(ancestral.state=0, sigma2=5, lambda=0.9))            
dat <- data.frame(trait=y[taxa], pred=x[taxa], pred2=x2[taxa],pred3=x3[taxa])
fit <- phylolm(trait ~ pred + pred2 + pred3, data=dat, phy=tre, model="lambda")
phyloglm.hp(fit,commonality=TRUE)
iv=list(env1="pred",env2=c("pred2","pred3"))
phyloglm.hp(fit,iv)
set.seed(123456)
tre <- rtree(50)
x1 <- rTrait(n=1, phy=tre)  
x2 <- rTrait(n=1, phy=tre)
x3 <- rTrait(n=1, phy=tre)
X <- cbind(rep(1, 50), x1, x2, x3)
y <- rbinTrait(n=1, phy=tre, beta=c(-1, 0.9, 0.9, 0.5), alpha=1, X=X)
dat <- data.frame(trait01=y, predictor1=x1, predictor2=x2, predictor3=x3)
fit <- phyloglm(trait01 ~ predictor1 + predictor2 + predictor3, phy=tre, data=dat)
phyloglm.hp(fit)
iv=list(env1="predictor1",env2=c("predictor2","predictor3"))
phyloglm.hp(fit,iv)

phylolm.hp documentation built on Nov. 26, 2025, 9:06 a.m.