Hi,
In working with model selection algorithms in other stats packages, there are
often the following type of selection procedures:
1) Forward selection: Variables are added one at a time, starting with the
most significant [according to your selection criterion.] Variables are not
removed.
2) Stepwise selection: Like forward selection, but at each stage, variables
can be dropped or added.
.
.
etc.
The regsubsets function in leaps offers a) exhaustive search, b) forward
selection, c) backward selection or d) sequential replacement. I thought
forward would work according to definition 1) above, but I am pretty sure I
have encountered a situation where, for example:
The one-factor model includes variable A
The two-factor model includes variables D, E
The three-factor model included variables C,D,E
.
.
etc.
Is there a way to coerce the behavior in definition 1? That is, if the best one
factor model contains A, all multi-factor variants will also? In other words,
variables will be added but never removed?
Thank you very much.
James C. McGrath
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