Roger,
summary(multinomRob(list(y1 ~ x1 + x2,y2 ~ x1 + x2, y3 ~ 0),data=d,
print.level=1))
Walter Mebane
Roger Levy writes:
> Many thanks for pointing this out to me!
>
> I'm still a bit confused, however, as to how to use multinomRob. For
> example I tried to translate the following example using nnet:
>
>
> x1 <- c(1,1,1,1,0,0,0,0,0,0,0,0)
> x2 <- c(0,0,0,0,1,1,1,1,0,0,0,0)
> y <- factor(c("a","b","b","c","a","b","c","c","a","a","b","c"))
> library(nnet)
> d <- data.frame(x1,x2,y)
> summary(multinom(y ~ x1 + x2, data=d))
>
>
> into multinomRob as follows:
>
>
> x1 <- c(1,1,1,1,0,0,0,0,0,0,0,0)
> x2 <- c(0,0,0,0,1,1,1,1,0,0,0,0)
> y <- factor(c("a","b","b","c","a","b","c","c","a","a","b","c"))
> y1 <- ifelse(y=="a",1, 0)
> y2 <- ifelse(y=="b", 1, 0)
> y3 <- ifelse(y=="c", 1, 0)
> d <- data.frame(x1,x2,y,y1,y2,y3)
> summary(multinomRob(list(y1 ~ x1 + x2,y2 ~ x1 + x2, y3 ~ x1 + x2),data=d))
>
> but the last command gives me the error message:
>
>
> [1] "multinomMLE: Hessian is not positive definite"
> Error in obsformation %*% opg : non-conformable arguments
>
>
> though it's not obvious to me why. I also tried a couple other variants:
>
>
> > summary(multinomRob(list(y1 ~ 0,y2 ~ x1 + x2,y3 ~ x1 + x2),data=d))
> Error in multinomT(Yp = Yp, Xarray = X, xvec = xvec, jacstack =
> jacstack, :
> (multinomT): invalid specification of Xarray (regressors not
> allowed for last category
> > summary(multinomRob(list(y1 ~ 0,y2 ~ x1 ,y3 ~ x2),data=d))
> Error in multinomT(Yp = Yp, Xarray = X, xvec = xvec, jacstack =
> jacstack, :
> (multinomT): invalid specification of Xarray (regressors not
> allowed for last category
>
>
> Any advice would be much appreciated!
>
>
> Many thanks,
>
> Roger
>
> Walter Mebane wrote:
> > By default, with print.level=0 or greater, the multinomRob program
> > prints the maximum likelihood estimates with conventional standard
> > errors before going on to compute the robust estimates.
> >
> > Walter Mebane
> >
> > Jasjeet Singh Sekhon writes:
> > >
> > > Hi Roger,
> > >
> > > Yes, multinomRob can handle equality constraints of this type---see
> > > the 'equality' option. But the function assumes that the outcomes are
> > > multinomial counts and it estimates overdispersed multinomial logistic
> > > models via MLE, a robust redescending-M estimator, and LQD which is
> > > another high breakdown point estimator. It would be a simple matter
> > > to edit the 'multinomMLE' function to work without counts and to do
> > > straight MNL instead, but right now it estimates an overdispersed MNL
> > > model.
> > >
> > > Cheers,
> > > Jas.
> > >
> > > =======================================
> > > Jasjeet S. Sekhon
> > >
> > > Associate Professor
> > > Travers Department of Political Science
> > > Survey Research Center
> > > UC Berkeley
> > >
> > > http://sekhon.berkeley.edu/
> > > V: 510-642-9974 F: 617-507-5524
> > > =======================================
> > >
> > >
> > >
> > > Roger Levy writes:
> > > > I'm interested in doing multinomial logistic regression with
> > equality
> > > > constraints on some of the parameter values. For example, with
> > > > categorical outcomes Y_1 (baseline), Y_2, and Y_3, and covariates
> > X_1
> > > > and X_2, I might want to impose the equality constraint that
> > > >
> > > > \beta_{2,1} = \beta_{3,2}
> > > >
> > > > that is, that the effect of X_1 on the logit of Y_2 is the same as
> > the
> > > > effect of X_2 on the logit of Y_3.
> > > >
> > > > Is there an existing facility or package in R for doing this? Would
> > > > multinomRob fit the bill?
> > > >
> > > > Many thanks,
> > > >
> > > > Roger
> > > >
> > > >
> > > > --
> > > >
> > > > Roger Levy Email: [EMAIL PROTECTED]
> > > > Assistant Professor Phone: 858-534-7219
> > > > Department of Linguistics Fax: 858-534-4789
> > > > UC San Diego Web: http://ling.ucsd.edu/~rlevy
> > > >
> > > >
> >
--
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Walter R. Mebane, Jr. email: [EMAIL PROTECTED]
Professor office voice: 607/255-3868
Department of Government cell: 607/592-0546
Cornell University fax: 607/255-4530
217 White Hall WWW: http://macht.arts.cornell.edu/wrm1/
Ithaca, NY 14853-7901
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