problem-11.22
problem-11.22
We fit the ANCOVA model first:
> library(MASS) # for stepAIC
> kid.weights$BMI = (kid.weights$weight/2.54)/ (kid.weights$height*2.54/100)^2
> res.full = lm(BMI ~ age + gender, kid.weights)
> res.age = lm(BMI ~ age, kid.weights)
> res.gender = lm(BMI ~ gender, kid.weights)
> anova(res.full, res.age)
Analysis of Variance Table
Model 1: BMI ~ age + gender
Model 2: BMI ~ age
Res.Df RSS Df Sum of Sq F Pr(>F)
1 247 9686
2 248 9686 -1 -0.25 0.01 0.94
> anova(res.full, res.gender)
Analysis of Variance Table
Model 1: BMI ~ age + gender
Model 2: BMI ~ gender
Res.Df RSS Df Sum of Sq F Pr(>F)
1 247 9686
2 248 9722 -1 -37 0.93 0.33
> stepAIC(res)
...
Call:
lm(formula = BMI ~ 1, data = kid.weights)
Coefficients:
(Intercept)
17
We see that neither variable is significant by stepAIC().