[R] polr probit versus stata oprobit
Jean Eid
jeaneid at chass.utoronto.ca
Thu Nov 11 03:09:21 CET 2004
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.
Jean,
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
>
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