knitr::opts_chunk$set( collapse = TRUE, fig.width = 6, fig.asp = .4, warning = FALSE, message = FALSE, comment = "#>" ) library(marginaleffects) library(patchwork) library(ggplot2) theme_set(theme_minimal())

In a previous vignette, we introduced the "marginal effect" as a partial derivative. Since derivatives are only properly defined for continuous variables, we cannot use them to interpret the effects of changes in categorical variables. For this, we turn to *contrasts* between Adjusted predictions. In the context of this package, a "Contrast" is defined as:

The difference between two adjusted predictions, calculated for meaningfully different regressor values (e.g., College graduates vs. Others).

Consider a simple model with a logical and a factor variable:

library(marginaleffects) tmp <- mtcars tmp$am <- as.logical(tmp$am) mod <- lm(mpg ~ am + factor(cyl), tmp)

The `marginaleffects`

function automatically computes contrasts for each level of the categorical variables, relative to the baseline category (`FALSE`

for logicals, and the reference level for factors), while holding all other values at their mode or mean:

mfx <- marginaleffects(mod) summary(mfx)

The summary printed above says that moving from the reference category `4`

to the level `6`

on the `cyl`

factor variable is associated with a change of `r sprintf("%.3f", tidy(mfx)$estimate[2])`

in the adjusted prediction. Similarly, the contrast from `FALSE`

to `TRUE`

on the `am`

variable is equal to `r sprintf("%.3f", tidy(mfx)$estimate[1])`

.

We can obtain the same results using the `emmeans`

package:

library(emmeans) emm <- emmeans(mod, specs = "cyl") contrast(emm, method = "revpairwise") emm <- emmeans(mod, specs = "am") contrast(emm, method = "revpairwise")

In models with multiplicative interactions, the contrasts of a categorical variable will depend on the values of the interacted variable:

mod_int <- lm(mpg ~ am * factor(cyl), tmp)

We can now use the `newdata`

argument of the `marginaleffects`

function to compute contrasts for different values of the other regressors. As in the marginal effects vignette, the `typical`

function can be handy. Since we only care about the logical `am`

contrast, we use the `variables`

to indicate the subset of results to report:

marginaleffects(mod_int, newdata = typical(cyl = tmp$cyl), variables = "am")

Once again, we obtain the same results with `emmeans`

:

emm <- emmeans(mod_int, specs = "am", by = "cyl") contrast(emm, method = "revpairwise")

As described above, the `marginaleffects`

package includes limited support to compute contrasts. Users who require more powerful features are encouraged to consider alternative packages such as emmeans, modelbased, or ggeffects. These packages offer useful features such as automatic back-transforms, p value correction for multiple comparisons, and more.

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