Best way to plot interaction effects from a linear model
interaction, r, regression
Solution
The effects package has good ploting methods for visualizing the predicted values of regressions.
thedata<-data.frame(x=rnorm(20),f=rep(c("level1","level2"),10))
thedata$y<-rnorm(20,,3)+thedata$x*(as.numeric(thedata$f)-1)
library(effects)
model.lm <- lm(formula=y ~ x*f,data=thedata)
plot(effect(term="x:f",mod=model.lm,default.levels=20),multiline=TRUE)
Problem
In an effort to help populate the R tag here, I am posting a few questions I have often received from students. I have developed my own answers to these over the years, but perhaps there are better ways floating around that I don't know about. The question: I just ran a regression with continuous `y` and `x` but factor `f` (where `levels(f)` produces `c("level1","level2")`) ``` thelm <- lm(y~x*f,data=thedata) ``` Now I would like to plot the predicted values of `y` by `x` broken down by groups defined by `f`. All of the plots I get are ugly and show too many lines. My answer: Try the `predict()` function. ``` ##restrict prediction to the valid data ##from the model by using thelm$model rather than thedata thedata$yhat <- predict(thelm, newdata=expand.grid(x=range(thelm$model$x), f=levels(thelm$model$f))) plot(yhat~x,data=thethedata,subset=f=="level1") lines(yhat~x,data=thedata,subset=f=="level2") ``` Are there other ideas out there that are (1) easier to understand for a newcomer and/or (2) better from some other perspective?