glmm.hp

https://www.rdocumentation.org/packages/glmm.hp/versions/0.1-4
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glmm.hp is an R package that conducts hierarchical partitioning to calculate individual...
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### Install glmm.hp R Package

Source: https://www.rdocumentation.org/packages/glmm.hp/versions/0.1-4/index

Provides the command to install the glmm.hp package from CRAN. This is necessary to use the package's functionalities.

```r
install.packages('glmm.hp')
```

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### Calculate Marginal R2 for LM with custom R2 type and commonality analysis

Source: https://www.rdocumentation.org/packages/glmm.hp/versions/0.1-4/topics/glmm

This example shows how to use glmm.hp with a standard linear model (lm) object. It demonstrates specifying a different R2 type ('R2' instead of the default 'adjR2') and enabling commonality analysis by setting the 'commonality' argument to TRUE.

```R
mod3 <- lm(Sepal.Length ~ Petal.Length + Petal.Width + Petal.Length:Petal.Width, data = iris)
glmm.hp(mod3,type="R2")
glmm.hp(mod3,commonality=TRUE)
```

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### Plotting glmm.hp Objects in R

Source: https://www.rdocumentation.org/packages/glmm.hp/versions/0.1-4/topics/plot

This R code demonstrates how to plot objects created by the glmm.hp function. It shows generating a glmm.hp object from a mixed-effects model and plotting it, as well as plotting with different options like 'R2' type and custom colors.

```r
library(MuMIn)
library(lme4)
mod1 <- lmer(Sepal.Length ~ Petal.Length + Petal.Width +(1 | Species), data = iris)
a <- glmm.hp(mod1)
plot(a)
mod3 <- lm(Sepal.Length ~ Petal.Length+Petal.Width,data = iris)
plot(glmm.hp(mod3,type="R2"))
plot(glmm.hp(mod3,commonality=TRUE),color = c("#8DD3C7", "#FFFFB3"))
```

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### Calculate Marginal R2 for GLM using glmm.hp

Source: https://www.rdocumentation.org/packages/glmm.hp/versions/0.1-4/topics/glmm

Demonstrates calculating hierarchical partitioning of marginal R2 for a generalized linear model (GLM) using the glmm.hp function. It uses a fitted glm object and shows how to obtain and plot the results, similar to mixed-effect models.

```R
mod2 <- glm(Sepal.Length ~ Petal.Length + Petal.Width, data = iris)
r.squaredGLMM(mod2)
glmm.hp(mod2)
b <- glmm.hp(mod2)
plot(b)
plot(glmm.hp(mod2))
```

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### Calculate Marginal R2 for Mixed Models using glmm.hp

Source: https://www.rdocumentation.org/packages/glmm.hp/versions/0.1-4/topics/glmm

This function calculates the hierarchical partitioning of marginal R2 for generalized linear mixed-effect models. It takes a fitted model object from packages like lme4, nlme, or glmmTMB as input. Outputs include the R2 for the full model and a matrix detailing individual predictor effects and their contribution to the total marginal R2.

```R
library(MuMIn)
library(lme4)
mod1 <- lmer(Sepal.Length ~ Petal.Length + Petal.Width+(1|Species),data = iris)
r.squaredGLMM(mod1)
glmm.hp(mod1)
a <- glmm.hp(mod1)
plot(a)
```
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