Thank you Thomas for your answer. It was the weights that are giving me problems and I still have no idea why. i.e. when I try your example, everything work fine. However when I do not include the weights=Freq and [fw=Freq] in both softwares, I do get verry different results.
Jean, On Thu, 11 Nov 2004, Thomas Lumley wrote: > On Wed, 10 Nov 2004, Jean Eid wrote: > > > Dear Thomas, > > > > Where you also able to replicate the second example? (the exaample > > that I turned the housing data into numerical variables) That is the one > > that my estimates differ. > > > > I don't have your second example, but I get the same results from > polr(formula = Sat ~ as.numeric(Infl) + as.numeric(Type) + > as.numeric(Cont), data = housing, weights = Freq, method = "probit") > and > oprobit Sat Infl Type Cont [fw=Freq] > for example. > > -thomas > > > > > > On Wed, 10 Nov 2004, Thomas Lumley wrote: > > > >> On Wed, 10 Nov 2004, Jean Eid wrote: > >> > >>> Dear All, > >>> I have been struggling to understand why for the housing data in MASS > >>> library R and stata give coef. estimates that are really different. I also > >>> tried to come up with many many examples myself (see below, of course I > >>> did not have the set.seed command included) and all of my > >>> `random' examples seem to give verry similar output. For the housing data, > >>> I have changed the data into numeric vectors instead of factors/ordered > >>> factors. I did so to try and get the same results as in STATA and to have > >>> the housing example as close as possible to the one I constructed. > >>> > >>> I run a debian sid, kernel 2.4, R 2.0.0, and STATA version 8.2, MASS > >>> version 7.2-8. > >>> > >>> > >>> here's the example ( I assume that you have STATA installed and can run in > >>> batch mode, if not the output is also given below) > >>> > >> > >> That example shows the same results with Stata and polr() from MASS. > >> > >> For the housing data, I also get the same coefficients in Stata as with > >> polr(): > >> > >> In R: > >> library(MASS) > >> library(foreign) > >> write.dta(housing, file="housing.dta") > >> house.probit<-polr(Sat ~ Infl + Type + Cont, data = housing, weights = > >> Freq, method = "probit") > >> summary(house.probit) > >> ------------------------- > >> Re-fitting to get Hessian > >> > >> Call: > >> polr(formula = Sat ~ Infl + Type + Cont, data = housing, weights = Freq, > >> method = "probit") > >> > >> Coefficients: > >> Value Std. Error t value > >> InflMedium 0.3464233 0.06413706 5.401297 > >> InflHigh 0.7829149 0.07642620 10.244063 > >> TypeApartment -0.3475372 0.07229093 -4.807480 > >> TypeAtrium -0.2178874 0.09476607 -2.299213 > >> TypeTerrace -0.6641737 0.09180004 -7.235005 > >> ContHigh 0.2223862 0.05812267 3.826153 > >> > >> Intercepts: > >> Value Std. Error t value > >> Low|Medium -0.2998 0.0762 -3.9371 > >> Medium|High 0.4267 0.0764 5.5850 > >> > >> Residual Deviance: 3479.689 > >> AIC: 3495.689 > >> ------------------------ > >> > >> > >> In Stata > >> ----------------- > >> . use housing.dta > >> . xi: oprobit Sat i.Infl i.Type i.Cont [fw=Freq] > >> i.Infl _IInfl_1-3 (naturally coded; _IInfl_1 omitted) > >> i.Type _IType_1-4 (naturally coded; _IType_1 omitted) > >> i.Cont _ICont_1-2 (naturally coded; _ICont_1 omitted) > >> > >> Iteration 0: log likelihood = -1824.4388 > >> Iteration 1: log likelihood = -1739.9254 > >> Iteration 2: log likelihood = -1739.8444 > >> > >> Ordered probit estimates Number of obs = 1681 > >> LR chi2(6) = > >> 169.19 > >> Prob > chi2 = > >> 0.0000 > >> Log likelihood = -1739.8444 Pseudo R2 = > >> 0.0464 > >> > >> ------------------------------------------------------------------------------ > >> Sat | Coef. Std. Err. z P>|z| [95% Conf. > >> Interval] > >> -------------+---------------------------------------------------------------- > >> _IInfl_2 | .3464228 .064137 5.40 0.000 .2207165 > >> .472129 > >> _IInfl_3 | .7829146 .076426 10.24 0.000 .6331224 > >> .9327069 > >> _IType_2 | -.3475367 .0722908 -4.81 0.000 -.4892241 > >> -.2058493 > >> _IType_3 | -.2178875 .094766 -2.30 0.021 -.4036254 > >> -.0321497 > >> _IType_4 | -.6641735 .0917999 -7.24 0.000 -.844098 > >> -.484249 > >> _ICont_2 | .2223858 .0581226 3.83 0.000 .1084676 > >> .336304 > >> -------------+---------------------------------------------------------------- > >> _cut1 | -.2998279 .0761537 (Ancillary parameters) > >> _cut2 | .4267208 .0764043 > >> ------------------------------------------------------------------------------ > >> > >> > >> > >> -thomas > >> > > > > ______________________________________________ > > [EMAIL PROTECTED] mailing list > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide! > > http://www.R-project.org/posting-guide.html > > > > Thomas Lumley Assoc. Professor, Biostatistics > [EMAIL PROTECTED] University of Washington, Seattle > ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
