Dear Dan
You can do it it R already - I do not know what the upper
limit on the number of variable is - I will send you some code tomorrow
when I tidy
the R script file up.
I gave a simple course on it at the ASSESS meeting in York in
October last and Susana gave a related talk.
Best
Gilbert
> Hi Susana,
>
> Thanks for your response. A few points of clarification (I'm afraid my
> initial posting may not have been clear).
>
> 1) There are only 3 variables in the example I provided. HOWEVER, I am
> interested in developing code to do this for any number of variables
> for future applications.
>
> 2) The variable list will be a mixture of continuous and categorical
> explanatory variables.
>
> 3) I was planning on using hit rates as my primary criterion for model
> selection instead of traditional model fit statistics (although I am
> currently gathering and tracking model fit stats too).
>
> 4) I already have SAS code to do all this for all possible main
> effects models. I just need to generalize it to consider all possible
> models including the interaction terms (apparently, I should have
> taken more computer science and combinatorics classes...).
>
> Any ideas?
>
> Thanks!
>
> Dan
>
>
>
>
> On Thu, Feb 9, 2012 at 10:53 AM, Susana Conde<[log in to unmask]> wrote:
>> Dear Abner,
>>
>> In non-hierarchical models, the Wald test statistics of the parameters
>> depend on the design matrix coding (i.e. one may find that they are non-zero
>> with some code, and then some of them happen to be zero with a different
>> code, for example).
>>
>> As it seems that you have "only" three variables, there are 19 possible
>> hierarchical models (provided that, if any interaction is added in the
>> model, all the interactions that include exactly the same variables but
>> considering different categories are also added): the 18 that are written in
>> your list below plus the null model).
>>
>> Gilbert and I would recommend to work only in the hierarchical class, if
>> possible.
>>
>> Best wishes,
>>
>> Susana
>>
>>
>>
>> On 09/02/2012 12:04, gilbert.mackenzie wrote:
>>> Dear Abner
>>>
>>> The technology is in R
>>>
>>> I will send you some details.
>>> Going out now.
>>>
>>> Recall that non-hierarchical models are of NO
>>> scientific interest as the results are not invariant
>>> to the choice of design matrix..
>>>
>>> Susana is expert.
>>>
>>> Best
>>>
>>> Gilbert
>>>
>>>
>>>
>>> The best way forward
>>>> Hello everyone,
>>>>
>>>> I need to write a program (either in SAS or R) that generates all
>>>> possible regression models WITH all possible combinations of
>>>> interactions (essentially the All Possible Regressions variable
>>>> selection method for OLS extended to binary logistic regression and
>>>> also extended to include all possible comibnations of interactions).
>>>>
>>>> If the input explanatory variable list was:
>>>>
>>>> VAR = GENDER ETHNICITY COUNTRY
>>>>
>>>> then I want a SAS data set or R data frame generated that looks the
>>>> table below. (BTW, these are all possible HIERARCHICAL models, there
>>>> are additional non-hierarchical combinations possible. Ideally the
>>>> program would be able to generate ALL possible combinations, but have
>>>> a macro parameter or function argument (e.g., hierarchical = TRUE)
>>>> which would identify and remove the non-hierarchical models from the
>>>> results).
>>>>
>>>> Model Number RHS
>>>>
>>>> 1 GENDER
>>>> 2 ETHNICITY
>>>> 3 COUNTRY
>>>> 4 GENDER ETHNICITY
>>>> 5 GENDER COUNTRY
>>>> 6 ETHNICITY COUNTRY
>>>> 7 GENDER ETHNICITY GENDER*ETHNICITY
>>>> 8 GENDER COUNTRY GENDER*COUNTRY
>>>> 9 ETHNICITY COUNTRY ETHNICITY*COUNTRY
>>>> 10 GENDER ETHNICITY COUNTRY
>>>> 11 GENDER ETHNICITY COUNTRY GENDER*ETHNICITY
>>>> 12 GENDER ETHNICITY COUNTRY GENDER*COUNTRY
>>>> 13 GENDER ETHNICITY COUNTRY ETHNICITY*COUNTRY
>>>> 14 GENDER ETHNICITY COUNTRY GENDER*ETHNICITY GENDER*COUNTRY
>>>> 15 GENDER ETHNICITY COUNTRY GENDER*ETHNICITY ETHNICITY*COUNTRY
>>>> 16 GENDER ETHNICITY COUNTRY GENDER*COUNTRY GENDER*COUNTRY
>>>> 17 GENDER ETHNICITY COUNTRY GENDER*ETHNICITY GENDER*COUNTRY
>>>> ETHNICITY*COUNTRY
>>>> 18 GENDER ETHNICITY COUNTRY GENDER*ETHNICITY GENDER*COUNTRY
>>>> ETHNICITY*COUNTRY GENDER*ETHNICITY*COUNTRY
>>>>
>>>> Any ideas?
>>>>
>>>> Thanks!
>>>>
>>>> Dan
>>>>
>>>> You may leave the list at any time by sending the command
>>>>
>>>> SIGNOFF allstat
>>>>
>>>> to [log in to unmask], leaving the subject line blank.
>>>>
>>>>
>>>
>>
>> --
>>
>>
>>
>>
>>
>> ------------------------------------------------------------------
>>
>> Post-Doctoral Researcher in Statistics,
>> ELDERMET Project
>>
>> Department of Statistics, Roinn na Staidhrimh,
>> School of Mathematical Sciences, Scoil na nEolaiochta Matamaitice,
>> Room 1.65, Seomra 1.65,
>> Western Gateway Building, Aras an Geata Thiar,
>> Western Road, Bothar Iarthar,
>> University College Cork, Coláiste na hOllscoile Corcaigh,
>> Cork, Ireland Corcaigh, Eire
>>
>> Tel. (+353 21 420) 5813 Fón. (+353 21 420) 5813
>> http://euclid.ucc.ie/pages/staff/Susana/Susana.htm
>>
>> -------------------------------------------------------------------
>>
>
--
_____________________________
Professor Gilbert MacKenzie
Centre of Biostatistics,
Room B2034
Dept. of Mathematics& Statistics,
University of Limerick,
Limerick
Ireland
CBS ~ http://www.ul.ie/biostatistics
BIO-SI ~ http://www.ul.ie/bio-si
Gilbert ~ http://www.staff.ul.ie/mackenzieg
Email: [log in to unmask]
Tel: 00 353 (0)61 213499
Fax: 00 353 (0)61 334927
Belfast (Home) Contact: 00 44 2890 682358
Professor MacKenzie was Visiting Professor of Statistics at ENSAI,
Rennes, France from January 2010 until May 2011.
ENSAI contact: [log in to unmask]
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