Jake Jake - 2 months ago 6
R Question

Best way to plot interaction effects from a linear model

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?

Answer Source

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)