Plotting one predictor of a model that has several predictors with ggplot

Question

Here is an typical example of linear model and a ggplot:

require(ggplot2)

utils::data(anorexia, package = "MASS")

anorex.1 <- glm(Postwt ~ Prewt + Treat + offset(Prewt),
                family = gaussian, data = anorexia)
coef(anorex.1)

   (Intercept)       Prewt   TreatCont     TreatFT 
     49.7711090  -0.5655388  -4.0970655   4.5630627 


ggplot(anorexia, aes(y=Postwt, x=Prewt)) + geom_point() + geom_smooth(method='lm', se=F)

My problem is that the regression that is made by geom_smooth(...) is not the same model than anorex.1 but is:

coef(lm(Postwt ~ Prewt, data=anorexia))

     (Intercept)       Prewt 
      42.7005802   0.5153804 

How can I plot the model anorexia1 on a ggplot?

Could I just take the intercept (49.77) and estimate (-0.5655) of anorexia1 for Prewt and plot it with geom_abline(..), is it correct? Is there a simpler solution?

Answer 1

As you have model that contains two predictors (different intercept values for levels) and also offset variable it won't e possible to directly include it in geom_smooth(). One way would be to make new data frame dat.new that contains values of Prewt for all three levels of Treat. Then use this new data frame to predict Postwt values for all levels using your model and add predicted values to new data frame

new.dat<-data.frame(Treat=rep(levels(anorexia$Treat),each=100),
                    Prewt=rep(seq(70,95,length.out=100),times=3))
anorexia.2<-data.frame(new.dat,Pred=predict(anorex.1,new.dat))
head(anorexia.2)
  Treat    Prewt     Pred
1   CBT 70.00000 80.18339
2   CBT 70.25253 80.29310
3   CBT 70.50505 80.40281
4   CBT 70.75758 80.51253
5   CBT 71.01010 80.62224
6   CBT 71.26263 80.73195

Now plot original points from the original data frame and add lines using new data frame that contains predictions.

ggplot(anorexia,aes(x=Prewt,y=Postwt,color=Treat))+geom_point()+
  geom_line(data=anorexia.2,aes(x=Prewt,y=Pred,color=Treat))