Re: st: using foreach in regression models

[Nick Cox <njcoxstata@gmail.com>]

(previous was premature post)

I doubt it's above your skills. It's just likely to be a novel trick
Consider the results of

local all frog toad newt
foreach beast of local all {
       local others : list all - beast
       di "`beast'"
       di "`others'"
}

Nick


>
> On Tue, Oct 25, 2011 at 10:26 AM, Tim Evans <Tim.Evans@wmciu.nhs.uk> wrote:
>> Nick,
>>
>> You are right, I don't actually need to run this:
>>
>> xi: logit treatmentinsccnetre2 i.agegrp i.NRCENT i.site_4 i.sex i.id07 i.yydx, iterate(10) or
>> xi: logit treatmentinsccnetre2 i.NRCENT i.site_4 i.sex i.agegrp i.id07 i.yydx, iterate(10) or
>> xi: logit treatmentinsccnetre2 i.id07 i.NRCENT i.site_4 i.sex i.agegrp i.yydx, iterate(10) or
>> xi: logit treatmentinsccnetre2 i.site_4 i.sex i.agegrp i.id07 i.NRCENT i.yydx, iterate(10) or
>>
>> Since the output will be the same as if I just run the first:
>>
>> xi: logit treatmentinsccnetre2 i.agegrp i.NRCENT i.site_4 i.sex i.id07 i.yydx, iterate(10) or
>>
>> Sorry for that. I did have a look at the local macros, but I need to spend some more time thinking about what you say as its currently above my programming skills.
>>
>> Best wishes
>>
>> Tim
>>
>> -----Original Message-----
>> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox
>> Sent: 25 October 2011 10:11
>> To: statalist@hsphsun2.harvard.edu
>> Subject: Re: st: using foreach in regression models
>>
>> It's hard to see why the order of variables makes a difference to the
>> model fitted.
>>
>> You should look at extended macro functions for list manipulation.
>> Start with -help macro-. The key is to have a local macro with
>> everything and subtract what you don't want each time.
>>
>> On Tue, Oct 25, 2011 at 10:03 AM, Tim Evans <Tim.Evans@wmciu.nhs.uk> wrote:
>>> Hi all,
>>>
>>> I am running a number of logistic regression models (Stata 11.2), using the same dependant variable, but cycling through a number of independent variables to obtain 'unadjusted' regressions like such:
>>>
>>> xi: logit treatmentinsccnetre2 i.agegrp, iterate(10) or
>>> xi: logit treatmentinsccnetre2 i.NRCENT, iterate(10) or
>>> xi: logit treatmentinsccnetre2 i.id07, iterate(10) or
>>> xi: logit treatmentinsccnetre2 i.site_4, iterate(10) or
>>>
>>> I can condense this down to the following
>>>
>>> foreach v of varlist agegrp id07 NRCENT site_4 {
>>> xi: logit treatmentinsccnetre2 i.`v', iterate(10) or
>>> }
>>>
>>> This is fine, however, I also do want to run the analysis by introducing a range of other variables in order to 'adjust' for their effect. The list of variables will always be the same, but although the list is the same, some variables move around the list - if they are the variable of interest i.e:
>>>
>>>
>>> xi: logit treatmentinsccnetre2 i.agegrp i.NRCENT i.site_4 i.sex i.id07 i.yydx, iterate(10) or
>>> xi: logit treatmentinsccnetre2 i.NRCENT i.site_4 i.sex i.agegrp i.id07 i.yydx, iterate(10) or
>>> xi: logit treatmentinsccnetre2 i.id07 i.NRCENT i.site_4 i.sex i.agegrp i.yydx, iterate(10) or
>>> xi: logit treatmentinsccnetre2 i.site_4 i.sex i.agegrp i.id07 i.NRCENT i.yydx, iterate(10) or
>>>
>>> What I would like to do is use the loop I employ for the unadjusted model, but be able to use something like `v' in order to condense the code from the above.
>>>
>>
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>

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