[R-sig-ME] ordinal regression with MCMCglmm
Kari Ruohonen
kari.ruohonen at utu.fi
Wed Apr 14 09:07:20 CEST 2010
Hi and thanks for the answer. I tried exactly that model syntax before
posting but the output of the "fixed" part had an unexpected
parameterisation and I thought I misspecified the model somehow. The
parameters I got with the above model are
- two cutpoints
- intercept
- effect of group B
I would have expected that instead of the intercept and two cutpoints I
would have had three cutpoints as given by polr (MASS package), for
example. Can you explain me the parameterisation in MCMCglmm and how it
connects to the one in polr that uses J-1 ordered cutpoints (J=number of
score classes) without an intercept?
Also, I am uncertain do I need to convert the "resp" before MCMCglmm to
an ordered factor (with "ordered")?
Many thanks,
Kari
On Tue, 2010-04-13 at 17:41 +0100, Jarrod Hadfield wrote:
> Hi Kari,
>
> The simplest model is
>
>
> m1<-MCMCglmm(resp~treat, random=~group, family="ordinal",
> data=your.data, prior=prior)
>
> as with multinomial data with a single realisation, the residual
> variance cannot be estimated from the data. The best option is to fix
> it at some value. most programs fix it at zero but MCMCglmm will fail
> to mix if this is done, so I usually fix it at 1:
>
>
> prior=list(R=list(V=1, fix=1), G=list(G1=list(V=1, nu=0)))
>
> I have left the default prior for the fixed effects (not explicitly
> specified above), and the default prior random effect variance
> structure (G) which has a zero degree of belief parameter. Often this
> requires some/more thought, especially if there are few groups or
> replication within groups is low. Sections 1.2, 1.5 & 8.2 in the
> CourseNotes cover priors for variances.
>
>
> Currently there is no option for specifying priors on the cut-points -
> the prior is flat and improper. The posterior in virtually all cases
> will be proper though.
>
> Cheers,
>
> Jarrod
>
> Quoting Kari Ruohonen <kari.ruohonen at utu.fi>:
>
> > Hi,
> > I am trying to figure out how to fit an ordinal regression model with
> > MCMCglmm. The "MCMCglmm Course notes" has a section on multinomial
> > models but no example of ordinal models. Suppose I have the following
> > data
> >
> > > data
> > resp treat group
> > 1 4 A 1
> > 2 4 A 1
> > 3 3 A 2
> > 4 4 A 2
> > 5 2 A 3
> > 6 4 A 3
> > 7 2 A 4
> > 8 2 A 4
> > 9 3 A 5
> > 10 2 A 5
> > 11 1 B 6
> > 12 1 B 6
> > 13 1 B 7
> > 14 2 B 7
> > 15 2 B 8
> > 16 3 B 8
> > 17 2 B 9
> > 18 1 B 9
> > 19 2 B 10
> > 20 2 B 10
> >
> > and the "resp" is an ordinal response, "treat" is a treatment and
> > "group" is membership to a group. Assume I would like to fit an ordinal
> > model between "resp" and "treat" by having "group" effects as random
> > effects. How would I specify such a model in MCMCglmm? And how would I
> > specify the prior distributions?
> >
> > All help is greatly appreciated.
> >
> > regards, Kari
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
> >
>
>
>
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