2
votes

My assignment is:

Use the regsubsets function in the leaps package to perform an exhaustive search

For best subsets regression models. Then compare the adjusted r^2 selected for each

Subset size. Which model is best according to this criterion? You will have to

Look at components of summary.regsubsets. There you can find the relevant.

Values of different optimality criteria for the best model selected at each size.

I have a data set cigs on which I call

q=regsubsets(Sales~Age+HS+Income+Black+Female+Price, data=cigs, method="exhaustive")

All of those are correct variables

summary(q)

returns

Subset selection object
Call: regsubsets.formula(Sales ~ Age + HS + Income + Black + Female + 
    Price, data = cigs, method = "exhaustive")
6 Variables  (and intercept)
       Forced in Forced out
Age        FALSE      FALSE
HS         FALSE      FALSE
Income     FALSE      FALSE
Black      FALSE      FALSE
Female     FALSE      FALSE
Price      FALSE      FALSE
1 subsets of each size up to 6
Selection Algorithm: exhaustive
         Age HS  Income Black Female Price
1  ( 1 ) " " " " "*"    " "   " "    " "  
2  ( 1 ) " " " " "*"    " "   " "    "*"  
3  ( 1 ) "*" " " "*"    " "   " "    "*"  
4  ( 1 ) "*" " " "*"    "*"   " "    "*"  
5  ( 1 ) "*" " " "*"    "*"   "*"    "*"  
6  ( 1 ) "*" "*" "*"    "*"   "*"    "*"

Any idea why this does not give me any information about r^2?

1

1 Answers

3
votes

What you see is the just a printed summary of your regsubsets object. If you type in ?regsubsets you see that summary.regsubsets returns more components than those printed. To access for example the r^2 just type:

summary_of_q <- summary(q)
summary_of_q$rsq