marginaleffects (version 0.5.0)

meffects: meffects() is a shortcut to marginaleffects()

Description

This function calculates marginal effects (slopes) for each row of the dataset. The resulting object can processed by the tidy() or summary() functions, which compute Average Marginal Effects (AME) or Group-Average Marginal Effects (G-AME). The datagrid() function and the newdata argument can be used to calculate Marginal Effects at the Mean (MEM) or Marginal Effects at User-Specified values (aka Marginal Effects at Representative values, MER). 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 Effects 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

meffects(
  model,
  newdata = NULL,
  variables = NULL,
  vcov = TRUE,
  conf_level = 0.95,
  type = "response",
  eps = 1e-04,
  ...
)

Value

A data.frame with one row per observation (per term/group) and several columns:

• rowid: row number of the newdata data frame

• type: prediction type, as defined by the type argument

• group: (optional) value of the grouped outcome (e.g., categorical outcome models)

• term: the variable whose marginal effect is computed

• dydx: marginal effect of the term on the outcome for a given combination of regressor values

• std.error: standard errors computed by via the delta method.

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 "marginal effect" is the partial derivative of the regression equation with respect to a variable in the model. This function uses automatic differentiation to compute marginal effects for a vast array of models, including non-linear models with transformations (e.g., polynomials). Uncertainty estimates are computed using the delta method.

A detailed vignette on marginal effects and a list of supported models can be found on the package website:

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

Numerical derivatives for the marginaleffects function are calculated using a simple epsilon difference approach: \(\partial Y / \partial X = (f(X + \varepsilon) - f(X)) / \varepsilon\), where f is the predict() method associated with the model class, and \(\varepsilon\) is determined by the eps argument.

Warning: Some models are particularly sensitive to eps, so it is good practice to try different values of this argument.

Standard errors for the marginal effects are obtained using the Delta method. See the "Technical Notes" vignette on the package website for details.

Examples

# NOT RUN {
mod <- glm(am ~ hp * wt, data = mtcars, family = binomial)
mfx <- marginaleffects(mod)
head(mfx)

# Average Marginal Effect (AME)
summary(mfx)
tidy(mfx)
plot(mfx)


# Marginal Effect at the Mean (MEM)
marginaleffects(mod, newdata = datagrid())

# Marginal Effect at User-Specified Values
# Variables not explicitly included in `datagrid()` are held at their means
marginaleffects(mod,
                newdata = datagrid(hp = c(100, 110)))

# Group-Average Marginal Effects (G-AME)
# Calculate marginal effects for each observation, and then take the average
# marginal effect within each subset of observations with different observed
# values for the `cyl` variable:
mod2 <- lm(mpg ~ hp * cyl, data = mtcars)
mfx2 <- marginaleffects(mod2, variables = "hp")
summary(mfx2, by = "cyl")

# Marginal Effects at User-Specified Values (counterfactual)
# Variables not explicitly included in `datagrid()` are held at their
# original values, and the whole dataset is duplicated once for each
# combination of the values in `datagrid()`
mfx <- marginaleffects(mod,
                       newdata = datagrid(hp = c(100, 110),
                                          grid_type = "counterfactual"))
head(mfx)

# Heteroskedasticity robust standard errors
marginaleffects(mod, vcov = sandwich::vcovHC(mod))

# }