phylolm.hp (version 0.0-3)

phyloglm.hp: 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)

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

Arguments

mod

Fitted phylolm or phyloglm model objects.

iv

optional the relative importance of predicotr groups will be evaluated. The input for iv should be a list containing the names of each group of variables. The variable names must be the names of the predictor variables in mod.

commonality

Logical; If TRUE, the result of commonality analysis is shown, the default is FALSE.

Author

Jiangshan Lai

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.

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.

• 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)