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Re: st: adjusted R2 in survey regression


From   "Aca N.T." <acant29@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: adjusted R2 in survey regression
Date   Thu, 16 Oct 2008 07:38:00 +0700

Thanks Steve!

Aca.

On Wed, Oct 15, 2008 at 10:11 PM, Steven Samuels
<sjhsamuels@earthlink.net> wrote:
> Below is a do-file which will calculate the adjusted R-square and related
> statistics after -svy: reg-.  The statistics are computed as temporary
> variables and are displayed with Nick Cox's -dlist- command, downloadable
> from SSC.  Be sure to zap gremlins in your text editor before running the
> code.  I agree with Maarten that theory-based choice of predictors is a
> necessity.
>
> -Steve
>
> **************************CODE BEGINS**************************
> /* Program to Compute Adjusted R Square for -svy: reg- */
> capture program drop _all
>
> webuse nhanes2, clear
> svy : reg weight height
> predict double resid, residual
>
> svy: mean weight resid   // substitute the response variable for "weight"
> here"
> tempvar v1 v2 sd1 sd2 adjr2
>
> gen `v1' = e(N)*el(e(V_srs),1,1)
> gen `sd1' = sqrt(`v1')
> gen `v2' = e(N)*el(e(V_srs),2,2)
> gen `sd2' = sqrt(`v2')
> gen `adjr2'= 100* (1-`v2'/`v1')
>
> label var `v1' "Est Pop Variance"
> label var `v2' "Est Residual Var"
> label var `sd1' "Est Pop SD"
> label var `sd2' "Est Resid SD"
> label var `adjr2' "Adjusted R-Sq (%)"
>
> format `v1' `v2' `sd1' `sd2' `adjr2' %10.1f
>
> dlist `v1' `v2' `sd1' `sd2' `adjr2' in 1, name(0)  // -dlist- by Nick Cox
> from SSC
> ***************************CODE ENDS***************************
>
>
>
> On Oct 15, 2008, at 4:35 AM, Maarten buis wrote:
>
>> --- "Aca N.T." <acant29@gmail.com> wrote:
>>>
>>> I'm puzzled with model building using -svy: reg- for there is no
>>> adjusted R squared produced.
>>> Is there an alternative test for this?
>>
>> Yes, it's called theory. Add the variable in whose effect you are
>> interested and add variables you think influence both the variable of
>> interest and the dependent variable. Do _not_ add variables that are
>> influenced by the variable of interest and in turn influence the
>> dependent variable. In other words add confounding variables but not
>> intervening variables.
>>
>> -- Maarten
>>
>>
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