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Re: st: Predicted values where a subgroup of variables are held constant
From
Tim Wade <[email protected]>
To
[email protected]
Subject
Re: st: Predicted values where a subgroup of variables are held constant
Date
Fri, 18 Mar 2011 13:46:05 -0400
Margins and adjust can allow practically any combination you want-How
about this?
sysuse auto.dta
logistic foreign mpg price weight length
*weight and length at means
adjust weight length, by(mpg price) gen(phat) pr
list mpg price if price==3291
*same result with margins
margins, atmeans at(mpg=20 price=3291)
Tim
On Fri, Mar 18, 2011 at 10:08 AM, Michael Harhay
<[email protected]> wrote:
> Hi Tim,
> Thank you for responding. I think this is close, but am having a tough
> time translating specifically to my model. I don't think it is exactly
> as I don't want to predict based on only one level of one co variate.
>
> let me try and outline better my case and sorry if it is the same.
>
> We are trying to predict an adjusted individual probability of being
> re-hospitalized.
>
> Our outcome is binary, but I was using OLS to try and work out the
> coding for the moment.
>
> Step 1: I regress rehosp using patient characteristics, hospital
> characteriscts and local market characteristics
>
> regress rehosp $patient $hospital $market
>
> my PI would like me to do something that would work like this if the
> code allowed
>
> predict y, xb at((means) hospital market)
>
> So we want to keep all the controls in the global command for hospital
> and market at their means but let the prediction vary at the
> individual level.
>
> -Michael
>
>
>
>
> On Fri, Mar 18, 2011 at 9:53 AM, Tim Wade <[email protected]> wrote:
>> Is something like this what you are looking for?
>>
>> sysuse auto.dta
>> regress price mpg gear_ratio length weight
>> *price with covariates at means and mpg at 20
>> margins, at((mean) _all mpg=20)
>> *generate predicted results at means for each observation of mpg
>> adjust gear_ratio length weight, gen(yhat)
>> *confirm result is same for margins
>> list yhat if mpg==20
>>
>>
>> Tim
>>
>>
>> On Thu, Mar 17, 2011 at 5:11 PM, Michael Harhay
>> <[email protected]> wrote:
>>> Dear all,
>>> I want to do a straightforward xb prediction after an OLS regression
>>> on a patient population, but for a subgroup of variables I want to use
>>> the sample mean not the true individual value for the prediction:
>>> I've been playing with both the "predict" and "margins" command but
>>> with no success. Is there an "if" like command I can put after predict
>>> to force this?
>>> After no success trying that I also tried to go about this in a roundabout way:
>>> preserve
>>> foreach var of local varlist `pred' {
>>> sum `var’, meanonly
>>> replace `var’ = r(mean)
>>> }
>>>
>>> predict sev5, xb
>>> The predictions are still not adjusted.
>>> Any quick thoughts?
>>> Thanks!
>>>
>>> *
>>> * For searches and help try:
>>> * http://www.stata.com/help.cgi?search
>>> * http://www.stata.com/support/statalist/faq
>>> * http://www.ats.ucla.edu/stat/stata/
>>>
>>
>
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/