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?

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.

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