RE: st: using foreach in regression models

[Tim Evans <Tim.Evans@wmciu.nhs.uk>]

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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