Re: st: tuples, stepwise and counting types of variables

[Joerg Luedicke <joerg.luedicke@gmail.com>]

If you are only interested in a prediction model, why do you even care
about parsimony? Why not just throwing in all of your predictor
variables?

J.

On Mon, Aug 13, 2012 at 7:24 PM, Thomas Sohnesen <sohnesen@gmail.com> wrote:
> Thanks Nick
>
> For this exercise i'm not interested in the coeffiicents or their
> meaning, i'm looking to find a parsimonouce model for predictions.
> Any advice on a better alternative than stepwise?  Doing it manually
> is not really an option as we will be running a lot of different
> models. Further, though my data is organized in blocks i would like to
> keep single variables if they are highly correlated with my dependent
> variable. I believe SAS has an alernative in MAXR. Do you know if
> stata has a similar alternativ?
>
> Finally, no matter which alternativ we end up using, i still have the
> challange of counting number of variables from each block in the final
> model. Any insights on that?
>
> thanks and best
>
> Thomas
>
>
> On Mon, Aug 13, 2012 at 5:30 PM, Nick Cox <njcoxstata@gmail.com> wrote:
>> I belong to a club which is dedicated to advising people against using
>> -stepwise-. A -search- will find an FAQ on this question.
>>
>> I'd look at -nestreg- instead.
>>
>> Nick
>>
>> On Mon, Aug 13, 2012 at 10:18 PM, Thomas Sohnesen <sohnesen@gmail.com> wrote:
>>
>>> I have a number of "groups" of variables as examplified below.
>>>
>>>
>>> local gr1  x1 x2 x3 x4
>>>
>>> local gr2  x5 x6 x7 x8
>>>
>>> local gr3  x9 x10 x11 x12 x13 x14 x15
>>>
>>> local gr4    x16 x17
>>>
>>>
>>>
>>> I run stepwise regressions for all the combinations of these groups
>>> using tuples.
>>>
>>> tuples "`gr1'" "`gr2'" "`gr3'" "`gr4'" , display
>>>
>>>                 forval i = 1/`ntuples' {
>>>
>>>  qui stepwise, pr(0.05):  regress y `tuple`i''
>>>
>>> }
>>>
>>>
>>>
>>> Now i would like to count how many variables from each group that
>>> stayed in the step wise model.
>>>
>>>
>>>
>>> For instance in the stepwise regression of gr1 and gr2  (ei x1 x2 x3
>>> x4   x5 x6 x7 x8) only x3 x4   x5 was included in the regression.  I
>>> would then like an output along the lines of:
>>>
>>> Model     Num_var_gr1     num_var_gr2  num_var_gr3  num_var_gr4
>>>
>>> gr2 gr3        1                           2                     0
>>>                 0
>>>
>>> gr2 gr4
>>>
>>> gr1 gr2
>>>
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