marginaleffects (version 0.3.2)

plot_cme: Plot Conditional Marginal Effects

Description

In models where two continuous variables are interacted, the marginal effect of one variable is conditional on the value of the other variable. This function draws a plot of the marginal effect of the effect variable for different values of the condition variable.

Usage

plot_cme(
  model,
  effect,
  condition,
  type = "response",
  conf.int = TRUE,
  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

Type(s) of prediction as string or vector This can differ based on the model type, but will typically be a string such as: "response", "link", "probs", or "zero".

conf.int

Logical indicating whether or not to include a confidence interval.

conf.level

The confidence level to use for the confidence interval if conf.int=TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

draw

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

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"))

# }