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Re: st: svy and pweight postestimation tools
Joao, You are right. -predict- with a -residuals- option does not
work after -svy: logistic-. I misread the -help- for logistic post-
estimation. About Carissa's note concerning design efects (DEFF's):
The strata and PSUs, when analyzed separately, provide design
effects almost equal to 1 so the effects in my model are almost
entirely from the weighting. So, I
could get results -except for standard errors - using just the
weights.
I'm not sure what she means. I would guess that she ignored the
sample weights and computed standard errors based only on clusters
and strata. However the unweighted estimates are likely to be
biased, so I doubt the relevance of the DEFF calculations. I think
the proper approach to assessing the contribution of strata and
clustering to DEFFS would be to start with weights, clusters and
strata, and then drop the strata and cluster specifications. I have
seen trade-offs in which clustering increased standard errors, offset
by stratification which reduced them. Moreover, DEFFs can vary by
model, outcome, predictor, and sub-population. Most samplers would
trim sample weights to reduce mean square error, or ignore them in
some cases, so generalizations are difficult (See Korn and Graubard,
Analysis of Health Surveys, Wiley, 1999, section 4.4
Carissa is correct in this regard: estimates of coefficients or of
population parameters depend only on the weights. However this is
independent of the DEFFs which ignore the weights. This is the basis
for my suggestion that she use -logistic- to compute ROC curves.
Note that Carissa can compute residuals for herself, starting with
the linear -xb- predictor and following formulas in the Stata 9
manual for logistic postestimation, page 90 The discussion starting
on page 105 in Korn and Graubard may also be helpful.
One amendment to my advice: The -linktest- that I showed may be of
limited usefulness to Carissa. -linktest- works best when some
covariates are continuous, but most of Carissa's are categorical.
On Nov 23, 2008, at 9:42 AM, Joao Ricardo F. Lima wrote:
Dear Steve,
I think that the option -predicit- with -residuals- is not valid in
Stata 10.1 too.
*****begin example*******
webuse nhanes2f
svyset psuid [pweight=finalwgt], strata(stratid)
svy: logistic diabetes sex race age
predict r, residuals
******end example*******
Please Steve, what is your opinion about her note:
"(Note: The strata and PSUs, when analyzed separately, provide design
effects almost equal to 1 so the effects in my model are almost
entirely from the weighting. So, I
could get results -except for standard errors - using just the
weights.)"
This make sense for you?
Thanx and Best Regards,
Joao Lima
2008/11/22 Steven Samuels <[email protected]>:
--
Carissa:
-help logistic postestimation- will show you which commands are
available
after -svy: logistic-. The -esttat clas- command is not one of
them in
Stata 9 or 10. -predict- with a -residuals- option is valid in
Stata 10.1
but not in Stata 9. You _can_ compute your own weighted survey -
linktest-
of fit.
predict hat, xb
gen hat2 = hat*hat
svy: logistic aepart hat hat2 //link test is the significance
of phat2
You can also construct ROC Curves. Use -logistic- with fweights,
the survey
weights rounded to the nearest integer. See the thread at:
http://www.stata.com/statalist/archive/2007-08/msg00739.html#_jmp0_ .
-Steve
On Nov 21, 2008, at 11:45 AM, Carissa Moffat Miller wrote:
StataList:
I am conducting logistic regression for a complex survey design
using
Stata version 9. I have found in your past discussions and the
user manuals
that many postestimation tests are not appropriate with svy
commands. I have
not found discussion on classification tables and residuals and
have been
unable to get the following commands to work either with an svy
command or
by just using the pweights in Stata.
I have been able to get these to work in another software program
using
the weights, but I'm concerned it isn't appropriately applied.
Can someone
tell me: 1) if these tests are appropriate with complex survey
data or just
pweights, and 2) if so,what are the commands or where would I
find them? or
3) if not appropriate, a reference I might cite?
(Note: The strata and PSUs, when analyzed separately, provide design
effects almost equal to
1 so the effects in my model are almost entirely from the
weighting. So, I
could get results -except for standard errors - using just the
weights.)
Cheers, Carissa
Syntax and error messages:
svyset APSU [pweight=FAWT], strata (ASTRATUM)
xi: svy: logistic aepart i.agecat i.Incomequ i.HIGHEDUC employed
female
urban
estat clas
{ERROR}: invalid subcommand clas
predict r, residuals
summarize r, detail
{ERROR}: option residuals not allowed
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--
----------------------------------------
Joao Ricardo Lima, D.Sc.
Professor
UFPB-CCA-DCFS
Fone: +553138923914
Skype: joao_ricardo_lima
----------------------------------------
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*
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* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/