On Thu, 9 Oct 1997, Matthew R. Nelson wrote:
> Dear R-users,
>
> Is anyone successfully using leaps() and particularly subsets()
> (contributed function) in R? When I attempt to run them with modest
> matrices, R promptly faults and dumps the core. Also, how does one use
> subsets.formula() within subsets()?
I haven't been able to cause problems like this (I'm running 0.50-a4 on
Solaris 2.5.1 and 0.50-a2 or thereabouts on Debian Linux).
What exactly happens, and do you mean "moderate"ly large or
"moderate"ly
small -- I just tried the following
R> x<-matrix(rnorm(10000),ncol=10)
R> subsets(x,y)->a
R> subsets(cbind(x,x),y)->aa
with 1000 cases and 10 variables and with 20 variables with 10 exact
linear dependencies.
With 50 variables and 1000 cases I get "Error: memory exhausted" but
this
can be fixed by specifying more memory at startup. With R -v20
R> x<-matrix(rnorm(50000),ncol=50)
R> y<-rnorm(1000)
R> subsets(cbind(x,x),y,method="backward")->aa
Exhaustive search with 50 variables is not feasible, but we can
try subsets up to size 4, say.
R> subsets(cbind(x,x),y,nvmax=4,really.big=T)->aa
This takes a long time, but doesn't crash on my system.
subsets.formula() is used automatically if the argument is a formula
object
R> p<-rnorm(1000)
R> q<-rep(0:1,500)
R> subsets(y~p*q)->a
R> summary(a)
Subset selection object
Call: subsets.formula(y ~ p * q)
3 Variables (and intercept)
Forced in Forced out
p FALSE FALSE
q FALSE FALSE
p.q FALSE FALSE
1 subsets of each size up to 3
Selection Algorithm: exhaustive
p q p.q
1 ( 1 ) "*" " " " "
2 ( 1 ) "*" " " "*"
3 ( 1 ) "*" "*" "*"
As you can see it doesn't care about preserving hierarchical models - it
chooses p+p.q as the best model of size 2.
Thomas Lumley
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