Re: st: measures of fit using svy:regress

[Steven Samuels <sjhsamuels@earthlink.net>]

You can estimate adjusted r-square directly as  Adj R2 = 1- (residual  
SD^2)/var(Y)



Estimate of var(Y).  See:
       http://www.stata.com/support/faqs/stat/supweight.html
for an estimate of the SD of Y.

For an estimate of the Residual SD
Here's a method  I previously posted to compute the Residual SD and a  
CI for it.   If you incorporated the formula for the population  
variance into nlcom, you could get a CI for the ratio of (residual  
Var)/Var(Y) and thus a CI of your adjusted R2.

/***********************************CODE  
STARTS**********************************************************/
/* First, a program to compute a 95% CI after -nlcom- has estimated a  
parameter on the log scale*/

capture program drop _all
program antilog
local lparm  el(r(b),1,1)
local se    sqrt(el(r(V),1,1))
local bound  invttail(e(df_r),.025)*`se'
local parm  exp(`lparm')
local ll  exp(`lparm'  - `bound')
local ul  exp( `lparm' + `bound')
di  "parm =" `parm'  "    llim = " `ll'  "   ul = " `ul'
end

webuse nhanes2, clear
svyset
svy : reg weight height
predict predicted

gen resid= weight-predicted
gen resid2=resid*resid

svy: mean  resid2
nlcom .5*log(_b[resid2])   //residual SD
nlcom   log(_b[resid2])   //residual variance


antilog

/***************************CODE  
ENDS****************************************************/
On Dec 21, 2007, at 10:08 PM, jbowdoin@schoolph.umass.edu wrote:


> I am using svy: regress. Are there other measures of fit available  
> besides R-
> squared? Can I obtain the adjusted R-squared or root MSE?
>
> *
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> *   http://www.ats.ucla.edu/stat/stata/
*
*   For searches and help try:
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