Hello,
I have encountered a problem which may be arising from details of my data and
or the statistics I am trying to do, or may be arising due to the way leaps
works internally. Unfortunately, I am not yet savvy enough to tell why.
I can say that this statement works (or at least works to the degree I expect):
b <- regsubsets(FUND~.,data=all, intercept=TRUE, nbest=1, nvmax=8,
really.big=T, method="forward")
It does produce a warning:
Warning message:
In leaps.setup(x, y, wt = wt, nbest = nbest, nvmax = nvmax, force.in =
force.in, :
2 linear dependencies found
But I can appreciate that and can reduce my dataset to deal with it. However,
if I use this command:
b <- regsubsets(FUND~.,data=all, intercept=FALSE, nbest=1, nvmax=8,
really.big=T, method="forward")
I get the following:
Warning message:
In leaps.setup(x, y, wt = wt, nbest = nbest, nvmax = nvmax, force.in =
force.in, :
2 linear dependencies found
> b <- regsubsets(FUND~.,data=all, intercept=FALSE, nbest=1, nvmax=8,
> really.big=T, method="forward")
Reordering variables and trying again:
Error in if (any(index[force.out] == -1)) stop("Can't force the same variable
in and out") :
missing value where TRUE/FALSE needed
In addition: Warning messages:
1: In leaps.setup(x, y, wt = wt, nbest = nbest, nvmax = nvmax, force.in =
force.in, :
1 linear dependencies found
2: In sing$lindep[-1] & force.in :
longer object length is not a multiple of shorter object length
3: In sing$lindep | c(FALSE, force.out) :
longer object length is not a multiple of shorter object length
I can understand how the inclusion (or not) of an intercept can impact
regression significance, but I am not exactly sure how to interpret this
particular output and what may be going on in the background. Thanks in advance
for any insights.
James McGrath
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