Note that this message is cross-posted in cross-validated ...
http://stats.stackexchange.com/questions/68250/selection-of-regressors

Jean


On Sat, Aug 24, 2013 at 11:48 AM, beginner <paxkn@nottingham.ac.uk> wrote:

> I have a question about the package leaps which I am using for model
> selection.
>
> I would like to compare 4 different selection methods: forward, backward,
> stepwise and best subset. I used the code below:
>
> library(leaps) forward <- regsubsets(Response ~.,data = mydata, method =
> "forward", nbest=1)
> backward <- regsubsets(Response ~.,data = mydata, method = "backward",
> nbest=1) stepwise <- regsubsets(Response ~., data = mydata, method =
> "seqrep", nbest=1) best subset <- regsubsets(Response ~.,data = mydata,
> method = "forward", nbest=1)
>
>
> opt = par (mfrow =c(2,2)) plot(forward, scale = "adjr2", main = "Forward
> Selection") plot(backward, scale = "adjr2", main = "Backward Selection")
> plot(stepwise, scale = "adjr2", main = "Stepwise selection") plot(best
> subset, scale = "adjr2", main = "Best subset selection")
>
> Using these commands I obtained figures below:
> <http://r.789695.n4.nabble.com/file/n4674451/leaps.png>
>
> I am wondering why figure A and D are similar to each other (and also
> figure
> B aand C). I would expect different algorithms to select models in a
> different way. For instance models selected with forward selection method
> should be chosen based on the significance level/ AIC value. On the other
> hand models selected with best subset selection method should be chosen
> based on the sample satisitcs. I am also wondering why forward selection
> does not choose one variable at the time adding it to the exisitng model ?
> Also Fig B shows that backward selection starts with eight variables in the
> model. Why it does not start with all the variables and excludes one at the
> time ?
>
> I would be very grateful for these clarifications.
>
>
>
> --
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>
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