## Overview

This package has functions to calculate marginal effects from brms models ( http://paul-buerkner.github.io/brms/ ). A central motivator is to calculate average marginal effects (AMEs) for continuous and discrete predictors in fixed effects only and mixed effects regression models including location scale models.

This table shows an overview of currently supported models / features where “X” indicates a specific model / feature is currently supported. The column ‘Fixed’ means fixed effects only models. The column ‘Mixed’ means mixed effects models.

Distribution / Feature Fixed Mixed
Gaussian / Normal ✔️ ✔️
Bernoulli (logistic) ✔️ ✔️
Poisson ✔️ ✔️
Negative Binomial ✔️ ✔️
Gamma ✔️ ✔️
Beta ✔️ ✔️
Multinomial logistic
Multivariate models
Gaussian location scale models ✔️ ✔️
Natural log / square root transformed outcomes ✔️ ✔️
Monotonic predictors ✔️ ✔️
Custom outcome transformations

In general, any distribution supported by brms that generates one and only one predicted value (e.g., not multinomial logistic regression models) should be supported for fixed effects only models. Also note that currently, only Gaussian random effects are supported. This is not too limiting as even for Bernoulli, Poisson, etc. outcomes, the random effects are commonly assumed to have a Gaussian distribution.

Here is a quick syntax overview of how to use the main function, brmsmargins().

#### Fixed effects, continuous predictor.

h <- .001
ames <- brmsmargins(
object = model,
add = data.frame(x = c(0, h)),
contrasts = cbind("AME x" = c(-1 / h, 1 / h)),
effects = "fixedonly")

#### Mixed effects, discrete predictor.

ames <- brmsmargins(
object = model,
add = data.frame(x = c(0, 1)),
contrasts = cbind("AME x" = c(-1, 1)),
effects = "integrateoutRE")

ames$Summary ames$ContrastSummary

#### Mixed Effects Location Scale, continuous predictor

h <- .001
ames <- brmsmargins(
object = model,
add = data.frame(x = c(0, h)),
contrasts = cbind("AME x" = c(-1 / h, 1 / h)),
dpar = "sigma",
effects = "integrateoutRE")

ames$ContrastSummary #### Mixed Effects Location Scale, discrete predictor ames <- brmsmargins( object = model, at = data.frame(x = c(0, 1)), contrasts = cbind("AME x" = c(-1, 1)), dpar = "sigma", effects = "integrateoutRE") ames$Summary
ames$ContrastSummary Note that even on mixed effects models, it is possible to generate predictions and marginal effects from the fixed effects only, just by specifying effects = "fixedonly" but this is probably not a good idea generally so not shown by default. Also note that for all of these examples ames$Summary would have a summary of the averaged predicted values. These often are useful for discrete predictors. For continuous predictors, if the focus is on marginal effects, they often are not interesting. However, the at argument can be used with continuous predictors to generate interesting averaged predicted values. For example, this would get predicted values integrating out random effects for a range of ages averaging (marginalizing) all other predictors / covariates.

ames <- brmsmargins(
object = model,
at = data.frame(age = c(20, 30, 40, 50, 60)),
effects = "integrateoutRE")

ames\$Summary

## Installation

You can install the package from CRAN by running this code:

install.packages("brmsmargins")

Alternately, for the latest, development version, run:

remotes::install_github("JWiley/brmsmargins")

• Location scale models. Location scale models are models where both the location (e.g., mean) and scale (e.g., variance / residual standard deviation) are explicitly modeled as outcomes. These require use of distributional parameters dpar in brms. This vignette shows how to calculate marginal effects from location scale models for the scale part.