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From |
Ricardo Ovaldia <ovaldia@yahoo.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: SE erroes for xtmixed-predict fitted |

Date |
Thu, 1 Mar 2012 06:20:30 -0800 (PST) |

Thank you. Ricardo Ovaldia, MS Statistician Oklahoma City, OK --- On Wed, 2/29/12, Steve Samuels <sjsamuels@gmail.com> wrote: > From: Steve Samuels <sjsamuels@gmail.com> > Subject: Re: st: SE erroes for xtmixed-predict fitted > To: statalist@hsphsun2.harvard.edu > Date: Wednesday, February 29, 2012, 7:41 PM > Two corrections: > > The second model you fit was: > > Y_ij = a + a_j + u_i + b_i*j + > e_ij where the a_j's are fixed and the u, > b, and e terms are random. i indexes subjects and j= > 1,2,3,4 indexes the pills > > Steve > > > > This is exactly what I wished you'd shown us the first time, > Ricardo. > > To answer your questions: > > 1. Constant SEs for no random slope model? Yes > > You have a perfectly balanced data set: four pills with six > subjects per pill. Therefore the standard errors for the > fitted values will be identical, because they are based on > the same SDs and the same number of observations. This > is easier to see if you run: > *********************************************************** > tab pill, gen(pill) > xtmixed fat pill1 pill2 pill3 pill4, nocons || id:, nolog > *********************************************************** > > This is an equivalent model and fits the four fixed pill > means individually. > > If you compute se_fitted as before, it will equal > 17.3125, and > the constant standard error for the fitted pill means > 7.068 is se_fitted/sqrt(6), which you can verify. > > If, on the other hand, some subjects did not get all the > pills, then you would expect standard errors for pill > effects would differ, because the denominator would not > always be sqrt(6). You can see this if you delete an > observation. > > 2. Random "slope" model? > > ************************************* > xtmixed fat i.pill ||id: pill , nolog > ************************************* > > Here you are off the track. Pill is a categorical variable, > but the "||id: pill" treats it as a continuous variable and > fits a slope coefficient. > > If "i" indicates id and "j" is the value associated with > pill j you are fitting a model: > > Y_ij = a + a_j + u_i + b*j + > e_ij where the a_j's are fixed and the u > and e terms are random > > Formally, nothing prevents you from doing this, but it > doesn't make sense. Your standard error for even this model > was incorrect. The standard error for the fitted value > should contain a term for b^2*j^2. A similar term > appeared in the post that I referred you to. For an example > of a random slope model, see the pig weight example in the > Manual. > > > Steve > sjsamuels@gmail.com > > On Feb 29, 2012, at 8:32 AM, Ricardo Ovaldia wrote: > > Thank you Steve for your help and patience. I am sorry that > I was not clear with what I needed. What I was actually > looking for was an -ado- file that would do this instead of > what you had posted because I was not sure that I was > changing your code correctly to fit my model. Again I > am sorry, I did not mean to upset you and I really > appreciate your help very much. > > That said, here is a simple example that I am not sure it is > correct. > There are 6 cats each treated with 4 different pills: > > clear > input id str1 sex fat1 fat2 fat3 fat4 > 1 M 44.5 7.3 3.4 12.4 16.900 > 2 M 33.0 21.0 23.1 25.4 25.625 > 3 M 19.1 5.0 11.8 22.0 14.475 > 4 F 9.4 4.6 4.6 5.8 6.100 > 5 F 71.3 23.3 25.6 68.2 47.100 > 6 F 51.2 38.0 36.0 52.6 44.450 > end > > reshape long fat ,i(id) j(pill) > <output omitted> > > xtmixed fat i.pill ||id: , nolog > > <output omitted> > > . predict fitted, fitted > > . predict se_fix, stdp > > . predict se_u*, reses > > . gen se_fitted= sqrt(se_fix^2 + se_u1^2 ) > > . sum se_fitted > > Variable | Obs > Mean Std. Dev. > Min Max > -------------+-------------------------------------------------------- > se_fitted | 24 > 8.373251 > 0 8.373251 8.373251 > > SE are the same for all observations. > > Now for the random slope & intercept model: > > . drop fitted se_fix se_u1 se_fitted > > . xtmixed fat i.pill ||id: pill , nolog > > <output omitted> > > . predict fitted, fitted > > . predict se_fix, stdp > > . predict se_u*, reses > > . gen se_fitted= sqrt(se_fix^2 + se_u1^2 + se_u2^2) > > . sum se_fitted > > Variable | Obs > Mean Std. Dev. > Min Max > -------------+-------------------------------------------------------- > se_fitted | 24 > 8.655818 > .0559801 8.592957 8.737734 > > Did I modify your code correctly? > > Thank you again, > Ricardo > > Ricardo Ovaldia, MS > Statistician > Oklahoma City, OK > > --- On Tue, 2/28/12, Steve Samuels <sjsamuels@gmail.com> > wrote: > > From: Steve Samuels <sjsamuels@gmail.com> > Subject: Re: st: SE erroes for xtmixed-predict fitted > To: statalist@hsphsun2.harvard.edu > Date: Tuesday, February 28, 2012, 8:06 PM > > I don't know what you are looking at, but yes,I would > expect > the same or near identical standard errors under many > circumstances. > > When you averaged, you are ignoring the fact that > Stata's linear predictors are "best". I don't see show you > thought you could improve upon them. I suggest that you > look > at a book on longitudinal data. Three good ones are: > > Diggle, Heagerty, Liang and Zeger, Anal of Long data 2nd Ed > Verbeke and Molenbergs Linear mixed models for > longitudinal analysis > Fitzmaurice, Laird,Ware, Applied Longitudinal Data > > but there are many others. > > Finally, please follow the FAQ in the future and show > exactly what you did and what Stata replied. It is > quite irritating to be told that you had searched and found > no answer to your question, when, apparently you had found > an answer, but just didn't like what it showed. > > Steve > sjsamuels@gmail.com > > On Feb 28, 2012, at 7:46 PM, Ricardo Ovaldia wrote: > > Thank you Steve. > > I did see Steve's post, however when I tried doing this, I > got the same SE for every patient when I leave out the > random slope term i.e.: > > xtmixed cholest i.drug month || patid > > So I was not sure that it was working correctly. When I > included the random slope term, I get slightly different > values for each patient but still very similar to each > other. Is that what I should expect? > > Ricardo > > Ricardo Ovaldia, MS > Statistician > Oklahoma City, OK > > --- On Tue, 2/28/12, Steve Samuels <sjsamuels@gmail.com> > wrote: > > From: Steve Samuels <sjsamuels@gmail.com> > Subject: Re: st: SE erroes for xtmixed-predict fitted > To: statalist@hsphsun2.harvard.edu > Cc: rgutierrez@stata.com > Date: Tuesday, February 28, 2012, 4:37 PM > You must have missed http://www.stata.com/statalist/archive/2011-12/msg00852.html > > where I tried to answer a related questionn. I'm not > sure > I'm right. If I'm not, I bet that Bobby Gutierrez > knows. > > Steve > sjsamuels@gmail.com > > On Feb 28, 2012, at 3:50 PM, Ricardo Ovaldia wrote: > > I searched the Statalist archives for the answer to > the > following query and could not find one. Can someone > please > help. > > After using -xtmixed- to fit: > > xtmixed cholest i.drug month || patid:month > > I used -predict, fitted- to get predicted estimates > that > account for both the fixed and random effects. > I them average these over drug and month to get mean > predicted cholesterol values for each drug at each > time > point. > Is there a way to place a CI or to calculate SE for > these > estimates? > > Thank you, > Ricardo. > > > * > * 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/

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