marginaleffects (version 0.5.0)

comparisons: Contrasts Between Adjusted Predictions

Description

This function calculates contrasts (or comparisons) between adjusted predictions for each row of the dataset. The resulting object can processed by the tidy() or summary() functions, which compute Average Contrasts (see ?summary.marginaleffects). The newdata argument can be used to calculate a variety of contrasts, including "Contrasts at the Mean," "Contrasts at User-Specified values" (aka Contrasts at Representative values), "Contrasts in Marginal Means", "Adjusted Risk Ratios", and much more. 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

• Contrasts Vignette

• Supported Models

• Case Studies

  • Bayesian analyses with brms

  • Mixed effects models

  • Generalized Additive Models

  • Multinomial Logit and Discrete Choice Models

  • Tables and plots

  • Robust standard errors and more

  • Transformations and Custom Contrasts: Adjusted Risk Ratio Example

Usage

comparisons(
  model,
  newdata = NULL,
  variables = NULL,
  type = "response",
  vcov = TRUE,
  conf_level = 0.95,
  contrast_factor = "reference",
  contrast_numeric = 1,
  transform_pre = "difference",
  transform_post = NULL,
  interaction = NULL,
  eps = 1e-04,
  ...
)

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

A "contrast" is the difference between two adjusted predictions, calculated for meaningfully different regressor values (e.g., College graduates vs. Others). Uncertainty estimates are computed using the delta method.

Detailed vignettes on contrasts, marginal effects, predictions, and marginal means, as well as a list of supported models can be found on the package website:

https://vincentarelbundock.github.io/marginaleffects/

Examples

# NOT RUN {
library(marginaleffects)
library(magrittr)

# Linear model
tmp <- mtcars
tmp$am <- as.logical(tmp$am)
mod <- lm(mpg ~ am + factor(cyl), tmp)
comparisons(mod, contrast_factor = "reference") %>% tidy()
comparisons(mod, contrast_factor = "sequential") %>% tidy()
comparisons(mod, contrast_factor = "pairwise") %>% tidy()

# GLM with different scale types
mod <- glm(am ~ factor(gear), data = mtcars)
comparisons(mod, type = "response") %>% tidy()
comparisons(mod, type = "link") %>% tidy()

# Contrasts at the mean
comparisons(mod, newdata = "mean")

# Contrasts between marginal means
comparisons(mod, newdata = "marginalmeans")

# Contrasts at user-specified values
comparisons(mod, newdata = datagrid(am = 0, cyl = tmp$cyl))

# Numeric contrasts
mod <- lm(mpg ~ hp, data = mtcars)
comparisons(mod, contrast_numeric = 1) %>% tidy()
comparisons(mod, contrast_numeric = 5) %>% tidy()
comparisons(mod, contrast_numeric = c(90, 100)) %>% tidy()
comparisons(mod, contrast_numeric = "iqr") %>% tidy()
comparisons(mod, contrast_numeric = "sd") %>% tidy()
comparisons(mod, contrast_numeric = "minmax") %>% tidy()

# Adjusted Risk Ratio (see Case Study vignette on the website)
mod <- glm(vs ~ mpg, data = mtcars, family = binomial)
cmp <- comparisons(mod, transform_pre = "lnratioavg")
summary(cmp, transform_post = exp)

# Adjusted Risk Ratio: Manual specification of the `transform_pre`
cmp <- comparisons(mod, transform_pre = function(hi, lo) log(mean(hi) / mean(lo)))
summary(cmp, transform_post = exp)
# Interactions between contrasts
mod <- lm(mpg ~ factor(cyl) * factor(gear) + hp, data = mtcars)
cmp <- comparisons(mod, variables = c("cyl", "gear"))
summary(cmp)

# variable-specific contrasts
cmp <- comparisons(mod, variables = list(gear = "sequential", hp = 10))
summary(cmp)

# }