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rmarginal-effects

How can I extract marginal effects from an interaction term?

I am trying to extract the marginal effects from an interactive term that captures for the effects of a treatment X (X is coded as 1 or 0) on outcome Y (Y is coded in a scale from -10 to 10) moderated by variable A (A is coded between 0 and 10). However, I am not sure how to extract the marginal effects from the interaction terms at the highest and lowest measure of A

 m<-lm(Y~ X*A, data = combined)

Overall, I managed to generate plots for the marginal effect using the interplot function:

interplot(m = m, var1 = "X", var2 = "A", ci = 0.90)+
  ylab("X")+
  xlab("A")+
  theme_bw()+
  ggtitle("Figure 1. Effect of X on Y Moderated by A")+
  theme(plot.title = element_text(face = "bold"))+
  geom_hline(yintercept = 0, linetype = "dashed")

Additionally, I tried to use ggpredict to extract the marginal effects with 90% confidence interval at different levels of A:

margin1<- ggpredict(m, c ("X", "A"), ci = 0.90)

margin1

However, using ggpredict produces marginal coefficients that do not align with what I see in the summary for m, and do not align with the marginal effects plot. Instead, I get estimates that are clearly not accurate or precise. How can I extract the marginal effects as seen in the interplot?

Solution

  • Since you unfortunately don't provide sample data, here is a minimal example using the R built-in state.x77 dataset.

    fit <- lm(Income ~ Illiteracy * Murder, data = as.data.frame(state.x77))

    We are interested in the marginal effect of Illiteracy on Income 

    library(sjPlot)
plot_model(fit, type = "int")

    enter image description here

    Here plot_model uses the minimal and maximal values of Murder as the grouping levels (this is the default behavious, see the Plotting linteraction effects of Regression Models vignette for details).