Scatter Plots - R Graphics - page 123

lm_predicted <- predictvals(modlinear, "ageYear" , "heightIn" )
loess_predicted <- predictvals(modloess, "ageYear" , "heightIn" )
sp + geom_line(data = lm_predicted, colour = "red" , size = .8 ) +
geom_line(data = loess_predicted, colour = "blue" , size = .8 )
Figure 5-22. Left: a quadratic prediction line from an lm object; right: prediction lines from linear
(red) and LOESS (blue) models
For glm models that use a nonlinear link function, you need to specify type="response" to the
predictvals() function. This is because the default behavior is to return predicted values in
the scale of the linear predictors, instead of in the scale of the response (y) variable.
To illustrate this, we'll use the biopsy data set from the MASS library. As we did in Adding Fitted
Regression Model Lines , we'll use V1 to predict class . Since logistic regression uses values from
0 to 1, while class is a factor, we'll first have to convert class to 0s and 1s:
library(MASS) # For the data set
b <- biopsy
b$classn[b$class == "benign" ] <- 0
b$classn[b$class == "malignant" ] <- 1
Next, we'll perform the logistic regression:
fitlogistic <- glm(classn ~ V1, b, family = binomial)