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Re: st: svy and pweight postestimation tools
Carissa, consider ROC curves (the classification tables are not very
useful in my experience). ROC curves show the trade-off between
sensitivity and specificity. You would usually want population
estimates of these probabilities, so ignoring the weights wouldn't be
wise.
My previous post describes how you can compute residuals. These are
inherently unweighted, because observations with the same covariate
pattern will have the same predicted value, and so have only two
values of residuals (for events and non-events). If you are
comparing mean residuals, you might choose to weight them. See Korn
& Graubard, Analysis of Health Surveys, Wiley, 1999, pp 105-115.
-Steve
On Nov 23, 2008, at 10:40 AM, Carissa Moffat Miller wrote:
Steve and Joao,
Thank you for your suggestions and the information. I had
found the goodness of fit measure do file from your discussions
(svylogitgof)
and thought there might be something similar for the estat clas or
residuals for svy.
All I was trying to say in my note is that the strata and
PSUs account for so little difference in the outcome that if it
were possible
to run residuals or classification tables using just pweights, I
wanted to keep
that option open. Such as:
xi: logistic aepart i.agecat i.Incomequ i.HIGHEDUC female
[pweight=FAWT]
But it appears that I will have the same issues. Thank you
so much for your responses and help.
Carissa
Date: Sun, 23 Nov 2008 11:42:05 -0300
From: [email protected]
To: [email protected]
Subject: Re: st: svy and pweight postestimation tools
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 :
--
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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*
* For searches and help try:
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*
* For searches and help try:
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* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/