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.

plot_cme(
model,
effect,
condition,
type = "response",
conf.int = TRUE,
conf.level = 0.95,
draw = TRUE
)

## Arguments

model Model object Name of the variable whose marginal effect we want to plot on the y-axis 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(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". Logical indicating whether or not to include a confidence interval. 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. TRUE returns a ggplot2 plot. FALSE returns a data.frame of the underlying data.

## Value

A ggplot2 object

## Examples

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"))