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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: predicted probabilities |

Date |
Sun, 16 Nov 2008 09:10:40 +0000 (GMT) |

--- Mona Mowafi <mmowafi@hsph.harvard.edu> wrote: > I am seeking to attain predicted probabilities of my outcome (BMI > cats - normal, overweight, obese) for four main independent > variables. I am not sure how to do it, but here is what I have > tried: > > svyset [pweight=femaleweight], strata(order) psu(place) > > xi: svymlogit BMICAT i.AGECAT4 i.ED2 i.WB_pov i.ASSET_INDEX > i.PCAwealthindex i.FATHERED i.GENHEALTH_PAST, basecategory(2) nolog > svymlogit, rrr > > predict p1 p2 p3 > sort ED2 > by ED2: sum p1 > by ED2: sum p2 > by ED2: sum p3 > > Here are my main questions: > > 1) Does this syntax, does p1 refer to my reference outcome = normal > weight; p2= overweight, p3 = obese? I want to make sure that I am > interpreting what p1, p2, and p3 is properly. You can see what category the variables refer to by looking at the labels that -predict- has attached to them. You can see those by typing -desc p*- (which will describe all variables whose name start with p, if there are too many of those type -desc p1 p2 p3-). > 2) If I sort and sum by p1, p2, and p3 - is this giving me the mean > predicted probability of each of my three outcomes for all > individuals in each of those three sub-categories (of education, for > example, as seen above)? That is what I'm trying to do. Yes, but there is a subtle issue here: the differences between the educational categories may be due to the effect of education but can also be due to differences between the educational categories in the distribution of the other explanatory variables. For instance the lower educational categories will consist of individuals from a lower social background and these tend to have , and these tend a higher BMI. You can keep the other variables constant by first replacing the other variables by some number, e.g. the mean, and than predict, and than make the tables. Both methods are illustrated below (I used -table- in this examples as it creates more compact tables, but -by ...: sum...- will work too, another alternative would be -tabstat-). *---------------------- begin example --------------------- webuse nhanes2f, clear svyset psuid [pweight=finalwgt], strata(stratid) tab health svy: mlogit health rural black orace sex age // create predictions without keeping other variables constant predict pr*, pr // the labels show which variable belongs to which category desc pr* // comparing the average predicted probabilities with the observed percentages sum pr* tab health table race , c(m pr1 m pr2 m pr3 m pr4 m pr5) // creating predictions while keeping other variables constant // predicted probabilities of urban women of average age preserve sum age if e(sample), meanonly replace age = r(mean) replace female = 1 replace rural = 0 predict pra*, pr table race , c(m pra1 m pra2 m pra3 m pra4 m pra5) restore *--------------------- end example ------------------- (For more on how to use examples I sent to the Statalist, see http://home.fsw.vu.nl/m.buis/stata/exampleFAQ.html ) Hope this helps, Maarten ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room N515 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- * * 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: predicted probabilities***From:*"Joao Ricardo F. Lima" <jricardofl@gmail.com>

**References**:**st: predicted probabilities***From:*"Mona Mowafi" <mmowafi@hsph.harvard.edu>

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