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Re: Re: st: CI for adjusted mean |

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Thu, 25 Oct 2007 14:27:54 -0500 |

Rachele Capocchi <[email protected]> said: >> I tried: >> >> . adjust wei, by(rep) se ci >> >> and I obtained the adjusted means of the groups, but I don't know how >> I can obtain the overall mean. And Maarten Buis <[email protected]> answered with" > sysuse auto, clear > recode rep78 1/2 = 3 > xi: reg mpg i.rep78 wei > adjust wei, se ci > adjust wei, se ci by(rep78) > > <cut> Just to add extra light to what is happening . adjust wei which produces 21.2899 as the estimate, is the same as . adjust wei _Irep78_4 _Irep78_5 because -by()- was not specified so there is only one cell in the output table and the variables that were left "as is" in the first call were set to their mean within that cell (there is only one cell). -adjust- is asking -predict- to give the prediction when weight is 3032.029, _Irep78_4 is .26086957, and _Irep78_5 is .15942029. The estimation sample has 26% with rep78 at level 4, 16% at level 5, and the remaining 58% at level 3 (with level 3 omitted from the regression estimation). You may be wondering what it means to ask for a prediction where an indicator variable is set to .26 or .16 instead of something like 0 or 1. -regress- knows nothing about categorical variables. As far as it knows all variables are continuous. So -predict- after -regress- has no problem producing predictions for values of _Irep78_4 such as .26. With our -regress- example, you get the same answer produced by the -adjust- commands above by taking an appropriately weighted linear combination of the predictions found in . adjust wei, by(rep) which gives values of ---------------------- Repair | Record | 1978 | xb ----------+----------- 3 | 20.9262 4 | 20.7741 5 | 23.4563 ---------------------- When you multiply the estimates for each level of rep78 by the proportion of that level found in the data 20.9262*.57971014 + 20.7741*.26086957 + 23.4563*.15942029 it equals our overall estimate 21.2899 obtained earlier. You might want to see the following FAQ http://www.stata.com/support/faqs/stat/adjust.html which is related to this discussion. Here are some additional comments concerning leaving variables "asis" in -adjust-. With -regress-, what I am about to point out does not matter (because the mean of a linear combination is the linear combination of the means), but with other estimators such as -logit- it does matter. So, since Rachele is using -anova- and -regress- she may not care about what I am about to point out, but those using other models should be aware of the issue. Maarten already knows all about this -- he wrote a recent Stata Journal article Buis, M. L. 2007. predict and adjust with logistic regression. Stata Journal 7, Number 2, pages 221-226. where he presents . webuse lbw . gen black = race==2 . gen other = race==3 . logit low age lwt black other smoke and compares . predict p . tabstat p, statistics(mean) by(ht) to . adjust, pr by(ht) Maarten concludes the Stata Journal article saying: "... predict will give us the average predicted probability for someone with hypertension, whereas adjust will give us the predicted probability for someone with average values on age, lwt, black, other, and smoke for someone with hypertension. It is the difference between a typical predicted probability for someone within a group and the predicted probability for someone with typical values on the explanatory variables for someone within that group." The Stata Journal article explains why this is true and presents two pictures illustrating the point. See http://www.stata-journal.com/abstracts/st0127.pdf for the abstract of this Stata Journal article and http://www.stata.com/bookstore/sjj.html?issue=sjj7-2 to order it if you do not have a copy available to you. Ken Higbee [email protected] StataCorp 1-800-STATAPC * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: CI for adjusted mean***From:*Rachele Capocchi <[email protected]>

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