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From |
Nick Cox <njcoxstata@gmail.com> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: Tabout including all categories |

Date |
Thu, 29 Dec 2011 15:04:18 +0000 |

Expecting -tabout- to know about -fre- just won't work (both SSC). One alternative is -groups- (SSC). Nick On 28 Dec 2011, at 17:35, Elizabeth Knaster <ElizabethK@uihi.org> wrote:

Thanks for your reply. Yes, I meant to say "cells with zerofrequencies." Any ideas?Take care, Liz Elizabeth Knaster, MPH Project Coordinator Urban Indian Health Institute Seattle Indian Health Board Phone: 206-812-3032 Fax: 206-812-3044 Email: ElizabethK@uihi.orgSign up for the UIHI's Weekly Resource E-mail here or subscribe at http://www.uihi.org/. The Weekly Resource E-mail is UIHI's primary communication onopportunities for staff development, grant announcements and otherrelevant public health information.------------------------------ Date: Thu, 22 Dec 2011 20:22:56 +0000 From: Nick Cox <njcoxstata@gmail.com> Subject: Re: st: Tabout including all categories Showing zero values is not a problem with any tabulation command. Do you mean cells with zero frequencies? NickOn Thu, Dec 22, 2011 at 7:31 PM, Elizabeth Knaster <ElizabethK@uihi.org> wrote:Hello! I could use some help with tabout:I want to use tabout to produce tables with all categories of avariable, even if the value is equal to zero. I have installed frefrom SSC and have successfully used includelabeled, for example,"fre agecat, includelabeled" but I am unable to use"includelabeled" with tabout. Is there a way to incorporate fre andincludelabeled in the tabout syntax? Or is there some other way tohave tabout display all categories of a variable, including zero?This is the current code I am using, for reference: foreach var0 in sex agecat durdmcat dmtype BMIcat {tabout `var0' year using "AllSitesTrends.xls", append mi c(freqcol) f(0 3p) clab(N %)} Thanks, and happy holidays! Liz Elizabeth Knaster, MPH Project Coordinator Urban Indian Health Institute Seattle Indian Health Board Phone: 206-812-3032 Fax: 206-812-3044 Email: ElizabethK@uihi.orgSign up for the UIHI's Weekly Resource E-mail here or subscribe at http://www.uihi.org/. The Weekly Resource E-mail is UIHI's primary communication onopportunities for staff development, grant announcements and otherrelevant public health information.* * 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/ ------------------------------ Date: Thu, 22 Dec 2011 15:44:51 -0500 From: Eric N <eric.erictor@gmail.com> Subject: st: random effects models with weighted observations My understanding is that there have been some user defined models like xtregre2 since xtreg, re does not permit weighting of observations. Does anybody have experience with xtregre2 and some advice in using it. - --Eric * * 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/ ------------------------------ Date: Thu, 22 Dec 2011 15:56:37 -0500 From: Austin Nichols <austinnichols@gmail.com>Subject: Re: st: analysis of cluster of fungal infection in an ICU-unitroland andersson <rolandersson@gmail.com>: I meant that if you just want a test of whether a given type of infection is more likely after the same type, which you have already said you observed in a graph, you could run a simple mlogit. No infection could also be a category modeled, and you could include all the negative results. For example, here is a case where the null is true (no clustering implied by the DGP): clear range id 1 1000 1000 g type=ceil(uniform()*6) tsset id g lasttype=l.type mlogit type i.lasttype A more sensible analysis might use duration with exact times of tests and entry into into the ICU, as opposed to simple order of test for infection, and try to isolate the actual mechanism causing the observed clustering, perhaps using a competing risks analysis on time to infection (with leaving the ICU being a censoring event). A good model should incorporate a deep understanding of the science and setting, which I do not have for fungal infections in an ICU. I would suspect ceiling tiles before staff, for example, but you clearly have a reason for suspecting the staff of transmitting the infections. On Wed, Dec 21, 2011 at 5:50 PM, roland andersson <rolandersson@gmail.com> wrote:Austin<snip>I do not understand what you mean by "You could just run an -mlogit- of type on last type"?* * 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/ ------------------------------ Date: Thu, 22 Dec 2011 16:40:55 -0500 From: "Data Analytics Corp." <walt@dataanalyticscorp.com> Subject: Re: st: RE: Hierarchical Bayes with MCMC Hi, This is good news. I'll definitely look at the web site and pdf file. Thanks for the help, Walt ________________________ Walter R. Paczkowski, Ph.D. Data Analytics Corp. 44 Hamilton Lane Plainsboro, NJ 08536 ________________________ (V) 609-936-8999 (F) 609-936-3733 walt@dataanalyticscorp.com www.dataanalyticscorp.com _____________________________________________________ On 12/22/2011 6:23 AM, George Leckie wrote:Following on from Nick Cox's comment. Yes, you can fit multilevel logistic regression models by Bayesianestimation (MCMC) in Stata by using the runmlwin command to callthe MLwiNstatistical software package.You can also fit a wide range of other multilevel models by bothlikelihoodand Bayesian methods. We gave a talk on runmlwin at the recent UK Stata Users' Group, 17th Meeting (16th September 2011) http://www.bristol.ac.uk/cmm/media/runmlwin/London.pdfWe have also set up a runmlwin website for interested users whichcontainscomprehensive documentation, worked examples and an activediscussion forumhttp://www.bristol.ac.uk/cmm/software/runmlwin/In particular, see our examples page where there are sample datasets,do-files and log files showing you how to fit multilevel logistic regressions by MCMC as well as many other models. http://www.bristol.ac.uk/cmm/software/runmlwin/examples/ The command can be downloaded from SSC in the usual way . ssc install runmlwin, replaceWhile MLwiN is a commercial package, MLwiN is free to UK academics(thanksto ESRC funding body) and a fully functional 30-day free version ofMLwiNis available to all other users. Best wishes George * * 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/ ------------------------------ Date: Thu, 22 Dec 2011 18:08:05 -0400 From: Daniel Marcelino <dmsilv@gmail.com> Subject: st: capture results from tabulate Dear all, I looking for capture somehow the higher value showed in "Freq." column, as well the label of v3 for each city table. Any idea? bysort city : tabulate v3 [iw=weight] /*replication*/ clear input str2 city weight byte(v1 v2 v3) "a" .5 1 2 3 "a" .1 2 3 4 "a" .9 3 2 5 "a" .8 3 4 2 "a" .2 4 5 1 "a" .3 5 1 3 "b" .4 1 4 3 "b" .1 2 3 4 "b" .6 3 2 5 "b" .8 4 1 2 "b" .5 4 5 1 "b" .7 1 5 4 "c" .4 2 1 3 "c" .2 2 4 1 "c" .7 3 4 5 "c" .3 4 1 2 "c" .8 4 5 1 "c" .1 5 4 3 end * * 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/ ------------------------------ Date: Thu, 22 Dec 2011 22:16:48 +0000 From: Nick Cox <njcoxstata@gmail.com> Subject: Re: st: capture results from tabulate Use -contract- instead. Under -by:- only the last table is saved Nick On 22 Dec 2011, at 22:08, Daniel Marcelino <dmsilv@gmail.com> wrote:Dear all, I looking for capture somehow the higher value showed in "Freq." column, as well the label of v3 for each city table. Any idea? bysort city : tabulate v3 [iw=weight] /*replication*/ clear input str2 city weight byte(v1 v2 v3) "a" .5 1 2 3 "a" .1 2 3 4 "a" .9 3 2 5 "a" .8 3 4 2 "a" .2 4 5 1 "a" .3 5 1 3 "b" .4 1 4 3 "b" .1 2 3 4 "b" .6 3 2 5 "b" .8 4 1 2 "b" .5 4 5 1 "b" .7 1 5 4 "c" .4 2 1 3 "c" .2 2 4 1 "c" .7 3 4 5 "c" .3 4 1 2 "c" .8 4 5 1 "c" .1 5 4 3 end * * 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/ ------------------------------ Date: Thu, 22 Dec 2011 18:18:00 -0500 From: Steve Samuels <sjsamuels@gmail.com> Subject: Re: st: standard errors after xtmixed, predit.., fitted Correction: If q = 1 - p se_logit = se_p/(p*q) se_logit^2 = (se_p/(p*q))^2 Steve Jennyfer.If there are not many different regions at your highest level, Idoubt that you should be fitting each a random effect-in what senseare they random?; fixed effects for the highest levels wouldprobably be better.In addition to a covariance term (below), you will need to add aterm for the survey standard errors. If se_p is the estimatedsurvey standard error for a proportion p, then the squared standarderror for the logit to add would be: se_logit^2 = se_p^2/(p*(1-p)).And yes, compute interval endpoints on the logit scale and convertback with the invlogit() function. And no, back-transformed standarderrors (or SDs) need not look like those for the original data.*********************** sysuse auto, clear gen lprice = log(price) mean price lprice di exp(0.0455814) ******************You have an additional problem if the yearly estimates for a singlecountry are correlated by virtue of the survey design. I would haveconsidered adding a correlation structure to the residual s.Here is how to add the covariance contribution to the standard errorifor Stata -productivity- example. Note that the "atr" term is thehyperbolic arctangent, not the log, of the correlation.*********************************************** webuse productivity, clear xtmixed gsp private emp hwy water other unemp /// || region: || state: unemp, cov(unstructured) reml matrix list e(b) //names of terms scalar sd_err = exp([lnsig_e]_cons ) scalar sd_region = exp([lns1_1_1]_cons) scalar sd_state_u = exp([lns2_1_1]_cons) scalar sd_state = exp([lns2_1_2]_cons) scalar atrho = [atr2_1_1_2]_cons scalar rho = (exp(2*atrho)-1)/(exp(2*atrho)+1) scalar cov = rho*sd_state*sd_state_u scalar dir // check these quantities against results predict fitted, fitted predict se_fix, stdp gen se_fitted= /// sqrt(se_fix^2 +sd_region^2 + sd_state^2 /// + (unemp*sd_state_u)^2 +unemp*2*cov + sd_err^2) sum se_fit* *****************************************************When you post in the future, please describe what you really did(Statalist FAQ Section 3.3). It will save a lot of time. Just towarn you: I'll have only infrequent looks at Statalist for the next10 days.Steve sjsamuels@gmail.com On Dec 22, 2011, at 6:11 AM, Jennyfer Wolf wrote: Dear Steve, thanks again so much! I have data from different surveys (survey point estimates) and I use a term for unstructured covariance in my model: xtmixed wat_tot year_spline1*|| reg1: || reg2: || country2:year_cat, cov(unstructured). so I will add an error term for this in my calculation of the fitted standard error: scalar sd_cov = exp([atr3_1_1_2]_cons) Actually wat_tot (the dependent variable) is transformed with logit(), to restrict observations between 0 and 1 as I am modelling proportions. After "predict A, fitted" I use the inlogit() command to get the backtransformed estimates. However, I had problems to backtransform the standard errors because when I compared standard errors received without any transformation of the dependent variable and backtransformed standard errors received from a transformed dependent variable, these values were very different. Would you know a solution to this (is it correct to also backtransform the fitted standard error) and also (of course) I would like to restrict my confidence intervals to values between 0 and 1. One concern rests relating to the confidence intervals I calculate with the fitted standard errors: The model fits the individual country data very well and the predictions for the estimates and the fitted values seem very sensible, however, the standard error and the CIs calculated with the method you proposed for the fitted values are huge and actually take any sense away from making a prediction.. Does that mean I need to mak my model simpler? Thank you very much for the great support! Jennyfer 2011/12/22 Steve Samuels <sjsamuels@gmail.com>:Jennyfer,I misunderstood your request: my solution was for an observationchosen at random and it incorrectly omitted the residual SD term,to boot. Try this.******************************************* webuse productivity, clear xtmixed gsp private emp hwy water other unemp /// || region: || state: unemp matrix list e(b) //names of terms scalar sd_res = exp([lnsig_e]_cons) predict se_fix, stdp predict se_region se_state_u se_state, reses des se* //check against variable labels gen se_fitted = /// sqrt(se_fix^2 +se_region^2 + se_state^2 /// + (unemp*se_state_u)^2 +sd_res^2) ******************************************* I think that in your case the last three statements will be: ****************************************************************** predict se_region1 se_region2 se_country_year se_country, rses des se* //check against variable labels sqrt(se_fix^2 +se_region1^2 + se_region2^2 + /// + se_country^2 + (year_cat*se_country_year)^2 +sd_res^2) ******************************************************************Note that these statements assume that there is no correlationbetween the country and countryXyear random effects, which is whatyour model implies. If there is such correlation (and you can testfor it), then a covariance term must be added to the estimatedstandard error.If you happen to have sample survey data, then be sure to read thesection of Survey Data in the manual entry for -xtmixed-.Steve sjsamuels@gmail.com On Dec 21, 2011, at 10:01 AM, Jennyfer Wolf wrote: Thank you very much for your answer. I've tried it in many different variations but I guess there are problems with this approach: 1. the squared standard deviations that we are adding up are describing variation from the fixed effects but, when I understand right, not the error of the model 2. the CIs I need describe the uncertainty for the estimates for each country so countries with more datapoints have a narrower CI and also for future predictions the CI should get wider (which does not happen with the approach you suggested.I tried gllamm and used the "ci_marg_mu" command after "gllapred x,mumarg fsample" but this does not fit to my individual country data and still gives me the same CIs no matter how many survey points I have per country. Any more ideas on how to get confidence intervals after "xtmixed" and "predict x, fitted" for the predicted values in multilevel modeling? (Alternatively with gllamm) Thank you very, very much. Jennyfer 2011/12/17 Steve Samuels <sjsamuels@gmail.com>:Correction: I should not have included the SD for the error term,as it is not part of the fitted value.Here's an example more like yours, but with two levels, not three.I expect t

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**References**:**st: Tabout including all categories***From:*Elizabeth Knaster <ElizabethK@uihi.org>

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