问题
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?
回答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))
来源:https://stackoverflow.com/questions/20451927/plotting-one-predictor-of-a-model-that-has-several-predictors-with-ggplot