Best way to plot interaction effects from a linear model

Question

Hi All,

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?

Best,

Jake

Answer 1

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)

Answer 2

Huh - still trying to wrap my brain around expand.grid(). Just for comparison's sake, this is how I'd do it (using ggplot2):

thedata <- data.frame(predict(thelm), thelm$model$x, thelm$model$f)

ggplot(thedata, aes(x = x, y = yhat, group = f, color = f)) + geom_line()

The ggplot() logic is pretty intuitive, I think - group and color the lines by f. With increasing numbers of groups, not having to specify a layer for each is increasingly helpful.