lines() not properly displaying quadratic fit

Question

I'm simply trying to display the fit I've generated using lm(), but the lines function is giving me a weird result in which there are multiple lines coming out of one point.

Here is my code:

library(ISLR)
data(Wage)
lm.mod<-lm(wage~poly(age, 4), data=Wage)
Wage$lm.fit<-predict(lm.mod, Wage)

plot(Wage$age, Wage$wage)
lines(Wage$age, Wage$lm.fit, col="blue")

I've tried resetting my plot with dev.off(), but I've had no luck. I'm using rStudio. FWIW, the line shows up perfectly fine if I make the regression linear only, but as soon as I make it quadratic or higher (using I(age^2) or poly()), I get a weird graph. Also, the points() function works fine with poly().

Thanks for the help.

Answer 1

Because you forgot to order the points by age first, the lines are going to random ages. This is happening for the linear regression too; he reason it works for lines is because traveling along any set of points along a line...stays on the line!

plot(Wage$age, Wage$wage)
lines(sort(Wage$age), Wage$lm.fit[order(Wage$age)], col = 'blue')

Consider increasing the line width for a better view:

lines(sort(Wage$age), Wage$lm.fit[order(Wage$age)], col = 'blue', lwd = 3)

Answer 2

Just to add another more general tip on plotting model predictions:

An often used strategy is to create a new data set (eg newdat ) which contains a sequence of values for your predictor variables across a range of possible values. Then use this data to show your predicted values. In this data set, you have a good spread of predictor variable values, but this may not always be the case. With the new data set, you can ensure that your line represents evenly distributed values across the variable's range:

Example

newdat <- data.frame(age=seq(min(Wage$age), max(Wage$age),length=1000))
newdat$pred <- predict(lm.mod, newdata=newdat)
plot(Wage$age, Wage$wage, col=8, ylab="Wage", xlab="Age")
lines(newdat$age, newdat$pred, col="blue", lwd=2)