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From |
Steven Samuels <sjhsamuels@earthlink.net> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: svy and pweight postestimation tools |

Date |
Sun, 18 Jan 2009 13:49:32 -0500 |

Carissa,

-Steve **************************CODE BEGINS************************** sysuse auto,clear **************************************************** * Frequency weighted analysis **************************************************** logistic foreign mpg [fw=rep78] predict phat0 lroc [fw=rep78] **************************************************** * Probability weights **************************************************** svyset _n [pweight=rep78] quietly svy: logistic foreign mpg predict phat somersd foreign phat [pweight=rep78], tr(c) matrix b = e(b) local auc = b[1,1] di "Area under the Curve: " %6.5f `auc' **************************************************** * Graph ROC Curve with probability weights ****************************************************

** Add zero-zero to graph tempfile t1 save `t1' clear input spec sens 1 0 end append using `t1' gen ispec=1-spec

***************************CODE ENDS*************************** On Jan 17, 2009, at 5:23 PM, Carissa Moffat Miller wrote:

Steve,I was able to create the ROC curves using your advice aboutconverting the pweights to fweights. However, now a dissertationcommittee member has asked me to justify (provide documentation) ofthe legitimacy of doing such a conversion. Is the conversion justto put the pweight in a format that will be accepted by the ROCcommand and artificially calling it an "fweight"?I was not able to find this specific issue addressed in the belowreference and I have not been able to find another reference. Doyou have any suggested citations?CarissaFrom: sjhsamuels@earthlink.net Subject: Re: st: svy and pweight postestimation tools Date: Sun, 23 Nov 2008 12:13:01 -0500 To: statalist@hsphsun2.harvard.edu 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. Carissa2008/11/22 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 * * 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/-- ---------------------------------------- Joao Ricardo Lima, D.Sc. Professor UFPB-CCA-DCFS Fone: +553138923914 Skype: joao_ricardo_lima ---------------------------------------- * * 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/* * 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/

**Follow-Ups**:**Re: st: svy and pweight postestimation tools***From:*Steven Samuels <sjhsamuels@earthlink.net>

**References**:**RE: st: svy and pweight postestimation tools***From:*Carissa Moffat Miller <carissamiller@vandals.uidaho.edu>

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