| gam.hp {gam.hp} | R Documentation |
Hierarchical Partitioning of Adjusted R2 and Explained Deviance for Generalized Additive Models
Description
Hierarchical Partitioning of Adjusted R2 and Explained Deviance for Generalized Additive Models
Usage
gam.hp(mod, type = "dev", commonality = FALSE)
Arguments
mod |
Fitted "gam" model objects. |
type |
The type of R-square of gam, either "dev" or "adjR2", in which "dev" is explained deviance and "adjR2" is adjusted R-square, the default is "dev". |
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 adjusted R2 and explained deviance for Generalized Additive Models. The adjusted R2 and explained deviance are is the output of summary.gam()in mgcv package.
Value
dev |
The R2 for the full model. |
hierarchical.partitioning |
A matrix containing individual effects and percentage of individual effects towards total adjusted R2 and explained deviance 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.
Examples
library(mgcv)
mod1 <- gam(Sepal.Length ~ s(Petal.Length) + s(Petal.Width) + Sepal.Width,data = iris)
summary(mod1)
gam.hp(mod1)
gam.hp(mod1,type="adjR2")
gam.hp(mod1,commonality=TRUE)