marginaleffects (version 0.5.0)

marginalmeans: Marginal Means

Description

Compute estimated marginal means for specified factors. Marginal means are adjusted predictions, averaged across a grid of categorical predictors, holding other numeric predictors at their means. For more information, see the Details and Examples sections below, and in the vignettes on the marginaleffects website: https://vincentarelbundock.github.io/marginaleffects/

• Getting Started

• Marginal Means Vignette

Usage

marginalmeans(
  model,
  variables = NULL,
  variables_grid = NULL,
  vcov = TRUE,
  conf_level = 0.95,
  type = "response",
  transform_post = NULL,
  interaction = NULL,
  ...
)

Value

Data frame of marginal means with one row per variable-value combination.

Model-Specific Arguments

Some model types allow model-specific arguments to modify the nature of marginal effects, predictions, marginal means, and contrasts.

Package	Class	Argument	Documentation
brms	brmsfit	ndraws	brms::posterior_predict
		re_formula
lme4	merMod	include_random	insight::get_predicted
		re.form	lme4::predict.merMod
		allow.new.levels	lme4::predict.merMod
glmmTMB	glmmTMB	re.form	glmmTMB::predict.glmmTMB
		allow.new.levels	glmmTMB::predict.glmmTMB
		zitype	glmmTMB::predict.glmmTMB
mgcv	bam	exclude	mgcv::predict.bam
robustlmm	rlmerMod	re.form	robustlmm::predict.rlmerMod
		allow.new.levels	robustlmm::predict.rlmerMod

Details

This function begins by calling the predictions function to obtain a grid of predictors, and adjusted predictions for each cell. The grid includes all combinations of the categorical variables listed in the variables and variables_grid arguments, or all combinations of the categorical variables used to fit the model if variables_grid is NULL. In the prediction grid, numeric variables are held at their means.

After constructing the grid and filling the grid with adjusted predictions, marginalmeans computes marginal means for the variables listed in the variables argument, by average across all categories in the grid.

marginalmeans can only compute standard errors for linear models, or for predictions on the link scale, that is, with the type argument set to "link".

The marginaleffects website compares the output of this function to the popular emmeans package, which provides similar but more advanced functionality: https://vincentarelbundock.github.io/marginaleffects/

Examples

# NOT RUN {
library(marginaleffects)

# Convert numeric variables to categorical before fitting the model
dat <- mtcars
dat$cyl <- as.factor(dat$cyl)
dat$am <- as.logical(dat$am)
mod <- lm(mpg ~ hp + cyl + am, data = dat)

# Compute and summarize marginal means
mm <- marginalmeans(mod)
summary(mm)
# }