[R-sig-ME] ordinal regression with MCMCglmm
ONKELINX, Thierry
Thierry.ONKELINX at inbo.be
Wed Apr 14 16:25:12 CEST 2010
Dear Jarrod,
I'm working on a similar problem. Does it makes sense to calculate that
for the fixed effects only? Something like this:
pnorm(-Xb),
pnorm(cp[1] - Xb) - pnorm(Xb)
pnorm(cp[2] - Xb) - pnorm(cp[1] - Xb)
1 - pnorm(cp[2] - Xb)
Best regards,
Thierry
------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium
Research Institute for Nature and Forest
team Biometrics & Quality Assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.inbo.be
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say what the experiment died of.
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The plural of anecdote is not data.
~ Roger Brinner
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ensure that a reasonable answer can be extracted from a given body of
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~ John Tukey
> -----Oorspronkelijk bericht-----
> Van: r-sig-mixed-models-bounces at r-project.org
> [mailto:r-sig-mixed-models-bounces at r-project.org] Namens
> Jarrod Hadfield
> Verzonden: woensdag 14 april 2010 15:35
> Aan: Kari Ruohonen
> CC: Kari Ruohonen; r-sig-mixed-models at r-project.org
> Onderwerp: Re: [R-sig-ME] ordinal regression with MCMCglmm
>
> Hi,
>
> Imagine a latent variable (l) that conforms to the standard
> linear model.
>
> l = Xb+Zu+e
>
> The probabilities of falling into each of the four categories are:
>
>
> pnorm(-l)
>
> pnorm(cp[1]-l)-pnorm(-l)
>
> pnorm(cp[2]-l)-pnorm(cp[1]-l)
>
> 1-pnorm(cp[2]-l)
>
> where cp is the vector of cut-points with 2 elements. A 3
> cut-point model would be over-parameterised (unless the
> intercept is zero, which I presume is what polr does (?).
>
> The factors don't need to be ordered, the order is obtained
> from levels(resp). In the future, I may only allowed ordered
> factors to stop any accidents.
>
> Cheers,
>
> Jarrod
>
>
>
>
>
> On 14 Apr 2010, at 08:07, Kari Ruohonen wrote:
>
> > 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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> in Scotland, with registration number SC005336.
>
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> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
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