[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Predicted Probability after xtgee (urgent!!!) |

Date |
Thu, 19 Jul 2007 19:12:43 +0100 (BST) |

--- ckang2@gmail.com wrote: > Could you anyone teach me how to get predicted probability after > running xtgee? predicted values are usually obtained using the -predict- command. > With searching the archives, some reconmmend to use mfx. > For example, these are the results actually I got by running mfx > after xtgee. > > arginal effects after xtgee > y = xb (predict) ------------------------------------------------------------------------------ > variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] > X ---------+-------------------------------------------------------------------- > x1 | .002552 .00662 0.39 0.700 -.010432 .015536 > .580848 > x2 | 1.88e-09 .00000 1.79 0.073 -1.8e-10 3.9e-09 > 304447 > 1) how is it possible that x2's dy/dx is over 1? I think dy.dx is odd > ratio. Am I right? No, but close. Marginal effects play a similar rol as odds ratios: they show the strength of the effect. In this case it shows by how much the linear predictor changes when the explanatory variable changes one unit, while fixing all other variables at their mean if the size of the effect doesn't change over the unit range. You can see that by looking at the top line of the output where it says "y = xb (predict)". If they were odds ratios than the number could easily be larer than one (that would mean a positive effect, one would mean no effect, and between zero and one a negative effect, negative numbers are not possible). Changes in the linear predictor, the thing that is measured in the output, can easily be larger than one. Even if we compute marginal effects in terms of the probability it can still be larger than one, since the marginal effect assumes that the effect doesn't change (it is the first derivative or the slope evaluated at that point). Finally the number you are refering to isn't larger than one, it is: 1.88e-09, the e-09 part means that you have to move the decimal point 9 places to the left, so it is actually 0.00000000188. > 2) what does "X" in the last column of the first raw mean? In non-linear models you have to fix the values of the explanatory variables at some number (usually the mean). The X column shows the value at which the explanatory variable is fixed. > 3) is there any other way (STATA command like "prvalue" or "estsimp") > to get predicted probability varying across the change of x (I.v.)? Yes, the trick is that -predict- will work on a different dataset as long as it contains variables with the same variable names. So you can replace some variables with their mean or some other value, and using predict you will now get predicted values while some variables change and others are fixed at their mean. This is best explained using an example: *------------- begin example -------------------- use http://www.stata-press.com/data/r9/nlswork2.dta, clear xtgee union south hours collgrad age, i(idcode) t(year) family(binomial) preserve replace collgrad = 1 replace age = 25 collapse collgrad age, by(south hours) predict mu, mu label var mu "predicted prob of union membership" list in 1/20 twoway line mu hours if south == 1, sort || /* */ line mu hours if south == 0, sort /* */ legend(lab(1 "South") /* */ lab(2 "Non-south")) 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 ) In this example I quite drastically change the data in a way that I want to use to create a graph, but I do not want save it. This is what the -preserve- and -restore- commands are for. To get an idea about what the data look like once I changed it -list-ed the first 20 observations. I used -collapse- to make sure I had only one observation for each value of hours and south. This is a trick that results in graphs that use less memory. 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 Z434 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- ___________________________________________________________ Yahoo! Mail is the world's favourite email. Don't settle for less, sign up for your free account today http://uk.rd.yahoo.com/evt=44106/*http://uk.docs.yahoo.com/mail/winter07.html * * 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/

**References**:**st: Predicted Probability after xtgee (urgent!!!)***From:*ckang2@gmail.com

- Prev by Date:
**st: Urgency** - Next by Date:
**Re: st: svy: mean and descriptive tables** - Previous by thread:
**st: Predicted Probability after xtgee (urgent!!!)** - Next by thread:
**st: pseudo R2s for Generalized Linear Models** - Index(es):

© Copyright 1996–2016 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |