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---
title: "Plotting Interaction Effects of Regression Models"
author: "Daniel Lüdecke"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Plotting Interaction Effects of Regression Models}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r set-options, echo = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", dev = "png", fig.width = 7, fig.height = 3.5, message = FALSE, warning = FALSE)
options(width = 800, tibble.width = Inf)
if (!requireNamespace("sjmisc", quietly = TRUE) ||
!requireNamespace("ggplot2", quietly = TRUE) ||
!requireNamespace("haven", quietly = TRUE) ||
!requireNamespace("sjlabelled", quietly = TRUE)) {
knitr::opts_chunk$set(eval = FALSE)
}
```
This document describes how to plot marginal effects of interaction terms from various regression models, using the `plot_model()` function. `plot_model()` is a generic plot-function, which accepts many model-objects, like `lm`, `glm`, `lme`, `lmerMod` etc.
`plot_model()` allows to create various plot tyes, which can be defined via the `type`-argument. The default is `type = "fe"`, which means that fixed effects (model coefficients) are plotted. To plot marginal effects of interaction terms, call `plot_model()` with:
* `type = "pred"` to plot predicted values (marginal effects) for specific model terms, including interaction terms.
* `type = "eff"`, which is similar to `type = "pred"`, however, discrete predictors are held constant at their proportions (not reference level). It internally calls \code{\link[effects]{Effect}} via \code{\link[ggeffects]{ggeffect}}.
* `type = "emm"`, which is similar to `type = "eff"`. It internally calls \code{\link[emmeans]{emmeans}} via \code{\link[ggeffects]{ggemmeans}}.
* `type = "int"` to plot marginal effects of interaction terms in a more convenient way.
`plot_model()` supports [labelled data](https://cran.r-project.org/package=sjlabelled) and automatically uses variable and value labels to annotate the plot. This works with most regression modelling functions.
***Note:** For marginal effects plots, **sjPlot** calls functions from the [**ggeffects-package**](https://strengejacke.github.io/ggeffects/). If you need more flexibility when creating marginal effects plots, consider directly using the **ggeffects**-package.*
# Two-Way-Interactions
_Note: To better understand the principle of plotting interaction terms, it might be helpful to read the vignette on [marginal effects](plot_marginal_effects.html) first._
To plot marginal effects of interaction terms, at least two model terms need to be specified (the terms that define the interaction) in the `terms`-argument, for which the effects are computed. To plot marginal effects for three-way-interactions, all three terms need to be specified in `terms`.
A convenient way to automatically plot interactions is `type = "int"`, which scans the model formula for interaction terms and then uses these as `terms`-argument.
```{r}
library(sjPlot)
library(sjmisc)
library(ggplot2)
data(efc)
theme_set(theme_sjplot())
# make categorical
efc$c161sex <- to_factor(efc$c161sex)
# fit model with interaction
fit <- lm(neg_c_7 ~ c12hour + barthtot * c161sex, data = efc)
plot_model(fit, type = "pred", terms = c("barthtot", "c161sex"))
```
For `type = "int"`, no terms need to be specified. Note that this plot type automatically uses the first interaction term in the formula for the x-axis, while the second term is used as grouping factor. Furthermore, if continuous variables are used as second term, you can specify preset-values for this term with the `mdrt.values`-argument, which are then used as grouping levels.
In this example, the second term is a factor with two levels (male/female), so there is no need for choosing specific values for the moderator.
```{r}
plot_model(fit, type = "int")
```
To switch the terms, in this example _barthtot_ and _c161sex_, simply switch the order of these terms on the `terms`-argument and use `type = "pred"`.
```{r}
plot_model(fit, type = "pred", terms = c("c161sex", "barthtot [0, 100]"))
```
To switch the terms for plot-type `type = "int"`, you need to re-fit the model and change the formula accordingly, i.e. using _c161sex_ as first term in the interaction.
```{r}
# fit model with interaction, switching terms in formula
fit <- lm(neg_c_7 ~ c12hour + c161sex * barthtot, data = efc)
plot_model(fit, type = "int")
```
By default, for continuous variables, the minimum and maximum values are chosen as grouping levels, which are 0 and 100 - that's why the previous two plots are identical. You have other options as well, e.g. the mean-value and +/- 1 standard deviation (as suggested by Cohen and Cohen for continuous variables and popularized by Aiken and West 1991), which can be specified using `mdrt.values`.
```{r}
plot_model(fit, type = "int", mdrt.values = "meansd")
```
# Three-Way-Interactions
Since the `terms`-argument accepts up to three model terms, you can also compute marginal effects for a 3-way-interaction.
```{r}
# fit model with 3-way-interaction
fit <- lm(neg_c_7 ~ c12hour * barthtot * c161sex, data = efc)
# select only levels 30, 50 and 70 from continuous variable Barthel-Index
plot_model(fit, type = "pred", terms = c("c12hour", "barthtot [30,50,70]", "c161sex"))
```
Again, `type = "int"` will automatically plot the interaction terms, however, using `mdrt.values = "minmax"` as default - in this case, the "levels" 0 and 100 from continuous variable _barthtot_ are chosen by default.
```{r}
plot_model(fit, type = "int")
```
# References
Aiken and West (1991). _Multiple Regression: Testing and Interpreting Interactions._
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