marginaleffects (version 0.5.0)

plot_cme: Plot Conditional Marginal Effects

Description

This function plots marginal effects (y-axis) against values of predictor(s) variable(s) (x-axis and colors). This is especially useful in models with interactions, where the values of marginal effects depend on the values of "condition" variables.

Usage

plot_cme(
  model,
  effect,
  condition,
  type = "response",
  vcov = NULL,
  conf_level = 0.95,
  draw = TRUE,
  ...
)

Arguments

model

Model object

effect

Name of the variable whose marginal effect we want to plot on the y-axis

condition

String or vector of two strings. The first is a variable name to be displayed on the x-axis. The second is a variable whose values will be displayed in different colors.

type

string indicates the type (scale) of the predictions used to compute marginal effects or contrasts. This can differ based on the model type, but will typically be a string such as: "response", "link", "probs", or "zero". When an unsupported string is entered, the model-specific list of acceptable values is returned in an error message.

vcov

Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values:

• FALSE: Do not compute standard errors. This can speed up computation considerably.

• TRUE: Unit-level standard errors using the default vcov(model) variance-covariance matrix.

• String which indicates the kind of uncertainty estimates to return.

  • Heteroskedasticity-consistent: "HC", "HC0", "HC1", "HC2", "HC3", "HC4", "HC4m", "HC5". See ?sandwich::vcovHC

  • Heteroskedasticity and autocorrelation consistent: "HAC"

  • Other: "NeweyWest", "KernHAC", "OPG". See the sandwich package documentation.

• One-sided formula which indicates the name of cluster variables (e.g., ~unit_id). This formula is passed to the cluster argument of the sandwich::vcovCL function.

• Square covariance matrix

• Function which returns a covariance matrix (e.g., stats::vcov(model))

conf_level

numeric value between 0 and 1. Confidence level to use to build a confidence interval.

draw

TRUE returns a ggplot2 plot. FALSE returns a data.frame of the underlying data.

...

Additional arguments are passed to the predict() method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the marginaleffects website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the ?marginaleffects documentation for a non-exhaustive list of available arguments.

Value

A ggplot2 object

Examples

# NOT RUN {
mod <- lm(mpg ~ hp * wt, data = mtcars)
plot_cme(mod, effect = "hp", condition = "wt")

mod <- lm(mpg ~ hp * wt * am, data = mtcars)
plot_cme(mod, effect = "hp", condition = c("wt", "am"))

# }