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
x but factor
thelm <- lm(y~x*f,data=thedata)
Now I would like to plot the predicted values of
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
##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?
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$f)-1) library(effects) model.lm <- lm(formula=y ~ x*f,data=thedata) plot(effect(term="x:f",mod=model.lm,default.levels=20),multiline=TRUE)
Huh - still trying to wrap my brain around
expand.grid(). Just for comparison's sake, this is how I'd do it (using ggplot2):
thedata <- data.frame(predict(thelm), thelm$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.