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: rlevy@(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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