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| From | "Kieran McCaul" <Kieran.McCaul@uwa.edu.au> |
| To | <statalist@hsphsun2.harvard.edu> |
| Subject | RE: st: Understanding Factor variables - is order significant ? |
| Date | Wed, 26 May 2010 13:41:44 +0800 |
.... It's a bug, I believe. In the example below there are two dichotomous variables, x1 and x2. I declare the reference level for x1 to be x1=1 and leave x2 at its default, x2=0. If I fit x2#ib1.x1, all is well and x1=1/x2=0 is recognised as the reference level. If, on the other hand, I fit ib1.x1#x2, x1=1/x2=0 is recognised as the reference level and dropped, but x1=0/x2=0 is also dropped. clear set obs 50000 set seed 101954 generate x1 = runiform() > 0.6 generate x2 = runiform() > 0.7 generate xb = 2 + 0.75*x1 + 1.25*x2 + 1.5*x1*x2 generate exb = exp(xb) generate py = rpoisson(exb) * This is correct poisson py x2##ib1.x1 * this is also correct poisson py x2#ib1.x1 fvexpand x2#ib1.x1 return list * This is correct - explicity dropping x2=0/x1=1, the reference level. poisson py 0b.x2#0.x1 1.x2#0.x1 1.x2#1b.x1 * This is incorrect poisson py ib1.x1#x2 fvexpand ib1.x1#x2 return list * this is incorrect - explicity dropping x2=0/x1=1, the reference level. poisson py 0.x1#0b.x2 0.x1#1.x2 1b.x1#1.x2 * this is why. Both x1=0/x2=0 and x1=1/x2=0 are being dropped poisson py 0.x1#1.x2 1b.x1#1.x2 -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Michael N. Mitchell Sent: Wednesday, 26 May 2010 12:41 PM To: statalist@hsphsun2.harvard.edu Subject: Re: st: Understanding Factor variables - is order significant ? Dear Richard That is an excellent summary, and I agree completely. Thanks for patiently walking me through this! I hope others have enjoyed this :) . Michael N. Mitchell Data Management Using Stata - http://www.stata.com/bookstore/dmus.html A Visual Guide to Stata Graphics - http://www.stata.com/bookstore/vgsg.html Stata tidbit of the week - http://www.MichaelNormanMitchell.com On 2010-05-25 9.22 PM, Richard Williams wrote: >> This is a perplexing state of affairs! I don't know how to explain this! >> >> I hope someone can help explain! > > Michael, I am relieved that someone else is confused! Tweaking your code > a bit, > > use http://www.ats.ucla.edu/stat/stata/dae/poissonreg, clear > gen himath = math > 50 > poisson daysabs himath#male, nolog > poisson daysabs ib0.male#ib1.himath, nolog > poisson daysabs ib1.himath#ib0.male, nolog > poisson daysabs ib0.male##ib1.himath, nolog > poisson daysabs ib1.himath##ib0.male, nolog > > The first 2 models give the correct result -- LR Chi-square = 202.49. > The 3rd gives the incorrect result of LR chi-square = 144.99. The last 2 > commands, using ## instead of #, also give the correct results. > > In the few examples we've tried where we knew the correct answer, it > looks like interactions of the form b0.x1#b1.x2 worked fine but > b1.x2#b0.x1 caused problems. Further, plain old regress seems to work > fine regardless of how you do the interactions but ml techniques like > poisson and ologit have problems. > > I strongly suspect there is some sort of bug here. But if not, maybe > we'll get a fascinating explanation that will greatly add to our > understanding of interaction effects! > > ------------------------------------------- > Richard Williams, Notre Dame Dept of Sociology > OFFICE: (574)631-6668, (574)631-6463 > HOME: (574)289-5227 > EMAIL: Richard.A.Williams.5@ND.Edu > WWW: http://www.nd.edu/~rwilliam > > * > * 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/ * * 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/