| Type: | Package |
| Title: | Hierarchical Partitioning of Adjusted R2 and Explained Deviance for Generalized Additive Models |
| Version: | 0.0-4 |
| Date: | 2025-10-7 |
| Depends: | R (≥ 3.4.0),mgcv,ggplot2 |
| Maintainer: | Jiangshan Lai <lai@njfu.edu.cn> |
| Description: | Conducts hierarchical partitioning to calculate individual contributions of each predictor towards adjusted R2 and explained deviance for generalized additive models based on output of 'gam()' and 'bam()' in 'mgcv' package, applying the algorithm in this paper: Lai(2024) <doi:10.1016/j.pld.2024.06.002>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| Encoding: | UTF-8 |
| URL: | https://github.com/laijiangshan/gam.hp |
| RoxygenNote: | 7.3.1 |
| NeedsCompilation: | no |
| Packaged: | 2025-10-07 08:55:05 UTC; lai |
| Author: | Jiangshan Lai 
[aut, cre],
Jing Tang [aut] |
| Repository: | CRAN |
| Date/Publication: | 2025-10-07 09:10:02 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 |
Hierarchical Partitioning of Adjusted R2 and Explained Deviance for Generalized Additive Models (GAM and BAM)
Description
This function conducts hierarchical partitioning to calculate the individual contributions of each predictor
towards total adjusted R^2 and explained deviance for Generalized Additive Models fitted by either
gam or bam in the mgcv package.
Usage
gam.hp(mod, iv = NULL, type = "dev", commonality = FALSE, data = NULL)
Arguments
mod |
Fitted |
iv |
Optional. A list specifying groups of predictor variables for assessing group-wise relative importance.
Each element of the list should contain the names of variables belonging to a specific group, corresponding
to the predictor names defined in the model ( |
type |
Character. The type of R-square for GAM/BAM models, either |
commonality |
Logical; if |
data |
Optional. The dataset used to fit the model. If not provided, the function will attempt to extract
the data directly from the fitted |
Details
The function supports both gam and bam model objects.
It decomposes the total explained deviance or adjusted R^2 into unique and shared contributions of
individual predictors or groups of predictors using hierarchical partitioning.
The adjusted R^2 and explained deviance values are extracted from summary.gam() or summary.bam().
Value
dev |
The R2 (either explained deviance or adjusted R^2) for the full model. |
hierarchical.partitioning |
A matrix containing the individual effects and the percentage contribution of each predictor to total explained deviance or adjusted R^2. |
Author(s)
Jiangshan Lai lai@njfu.edu.cn
References
Lai J., Tang J., Li T., Zhang A., Mao L. (2024). Evaluating the relative importance of predictors in Generalized Additive Models using the gam.hp R package. *Plant Diversity*, 46(4): 542-546. <DOI:10.1016/j.pld.2024.06.002>
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. *The 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)
## Example 1: Using gam()
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)
iv <- list(env1 = c("s(Petal.Length)", "s(Petal.Width)"), env2 = "Sepal.Width")
gam.hp(mod1, iv, type = "adjR2")
gam.hp(mod1, iv, commonality = TRUE)
## Example 2: Using bam()
mod2 <- bam(Sepal.Length ~ s(Petal.Length) + s(Petal.Width) + Sepal.Width,
data = iris)
summary(mod2)
gam.hp(mod2)
gam.hp(mod2, type = "adjR2")
gam.hp(mod2, commonality = TRUE)
## Explicitly specifying data (useful for bam)
gam.hp(mod2, data = iris)
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 |
Permutation Test of Hierarchical Partitioning for GAM Analysis
Description
Permutation Test of Hierarchical Partitioning for GAM Analysis
Usage
permu.gamhp(mod = NULL, iv = NULL, type = "dev", permutations = 10)
Arguments
mod |
gam model generated by mgcv::gam() |
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 total explained variation, either "dev" or "adjR2", in which "dev" is deviance explained and "adjR2" is adjusted R-square, the default is "adjR2". |
permutations |
An integer; Number of permutations for computing p value of individual contribution for the randomized dataset. |
Details
This function is a permutation test of hierarchical partitioning for gam analysis. It returns a matrix of I values (the individual contribution towards total explained variation) for all values from permutations randomizations. For each permutation, the values in each variable (i.e each column of iv) are randomized independently, and gam.hp is run on the randomized iv. As well as the randomized I matrix, the function returns a summary table listing the observed I values, the p value of I for the randomized dataset.
Value
a data.frame containing a summary table listing the observed individual contribution, the p value of individual contribution for the randomized dataset
Author(s)
Jiangshan Lai lai@njfu.edu.cn
Examples
library(mgcv)
mod1 <- gam(Sepal.Length ~ s(Petal.Length) + s(Petal.Width) + Sepal.Width,data = iris)
permu.gamhp(mod=mod1,type="dev",permutations=10)
iv <- list(env1=c("s(Petal.Length)","s(Petal.Width)"),env2="Sepal.Width")
permu.gamhp(mod=mod1,iv,type="dev",permutations=10)
Plot for a gam.hp object
Description
Plot for a gam.hp object
Usage
## S3 method for class 'gamhp'
plot(x, plot.perc = FALSE, ...)
Arguments
x |
A |
plot.perc |
Logical;if TRUE, the bar plot (based on ggplot2 package) of the percentage to individual effects of variables towards total explained variation, the default is FALSE to show plot with original individual effects. |
... |
unused |
Value
a ggplot object
Author(s)
Jiangshan Lai lai@njfu.edu.cn
Examples
library(mgcv)
mod1 <- gam(Sepal.Length ~ s(Petal.Length) + s(Petal.Width) + Sepal.Width,data = iris)
plot(gam.hp(mod1))