Have you considered the use of Decision Trees?
DM
-------- Original Message --------
Subject: Re: All Possible Regressions with interaction terms for binary
logistic regression XXXX
From: Dan Abner <[log in to unmask]>
Date: Thu, February 09, 2012 10:12 am
To: [log in to unmask]
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
>>>
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>>>
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>>>
>>
>>
>
>
> --
>
>
>
>
>
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>
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> ELDERMET Project
>
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>
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