| Type: | Package |
| Title: | Hierarchical Partitioning of R2 for Phylogenetic Linear Regression |
| Version: | 0.0-3 |
| Date: | 2024-11-30 |
| Depends: | R (≥ 3.4.0),rr2,phylolm,ggplot2,vegan |
| Maintainer: | Jiangshan Lai <lai@njfu.edu.cn> |
| Description: | Conducts hierarchical partitioning to calculate individual contributions of phylogenetic tree and predictors (groups) towards total R2 for phylogenetic linear regression models. |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| Encoding: | UTF-8 |
| URL: | https://github.com/laijiangshan/phylolm.hp |
| RoxygenNote: | 7.3.1 |
| NeedsCompilation: | no |
| Packaged: | 2024-12-01 00:02:16 UTC; lai |
| Author: | Jiangshan Lai 
[aut, cre],
Gang Wang [aut] |
| Repository: | CRAN |
| Date/Publication: | 2024-12-05 15:40:02 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. |
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 |
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 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. |
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 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.
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)
Hierarchical Partitioning of R2 for Phylogenetic Linear Regression
Description
Hierarchical Partitioning of R2 for Phylogenetic Linear Regression
Usage
phylolm.hp(mod, iv = NULL, commonality = FALSE)
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. |
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 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.
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")
phylolm.hp(fit,commonality=TRUE)
iv=list(env1="pred",env2=c("pred2","pred3"))
phylolm.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)
phylolm.hp(fit)
iv=list(env1="predictor1",env2=c("predictor2","predictor3"))
phylolm.hp(fit,iv)
Plot for a phyloglm.hp object
Description
Plot for a phyloglm.hp object
Usage
## S3 method for class 'phyloglmhp'
plot(x, plot.perc = FALSE, commonality = FALSE, color = NULL, 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 and phylogenetic signal towards total explained variation, the default is FALSE to show plot with original individual effects. |
commonality |
Logical; If TRUE, the result of commonality analysis is shown, the default is FALSE. |
color |
Color of variables. |
dig |
Integer; number of decimal places in Venn diagram. |
... |
unused |
Value
a ggplot object
Author(s)
Jiangshan Lai lai@njfu.edu.cn
Examples
library(phylolm)
library(rr2)
set.seed(123456)
tre <- rtree(50)
x1 <- rTrait(n=1, phy=tre)
x2 <- rTrait(n=1, phy=tre)
X <- cbind(rep(1, 50), x1, x2)
y <- rbinTrait(n=1, phy=tre, beta=c(-1, 0.8, 0.9), alpha=1, X=X)
dat <- data.frame(trait01=y, predictor1=x1, predictor2=x2)
fit <- phyloglm(trait01 ~ predictor1 + predictor2, phy=tre, data=dat)
plot(phyloglm.hp(fit,commonality=TRUE))
plot(phyloglm.hp(fit,commonality=TRUE),commonality=TRUE)