[R-sig-ME] MCMCglmm troubles
Mike.Lawrence at dal.ca
Thu Jun 24 13:21:29 CEST 2010
Ah, that makes sense now. Oh, and I think it is indeed the
"multinomial3" family that I need to model; I used rnorm() as a
shortcut to generate data to show the error I encountered, but the
real data are counts.
On Thu, Jun 24, 2010 at 5:46 AM, Jarrod Hadfield <j.hadfield at ed.ac.uk> wrote:
> Hi Mike,
> Sorry for the delay on this. I'm not sure you want "multinomial3" as your
> family given that the three response variables are normally distributed.
> Specifying family=rep("gaussian", 3) would be more appropriate. You will
> still get an error message because the default residual term is ~units
> which specifies a residual for each row of the response. For univariate
> models this is equivalent to IID residuals. For multi-response models it is
> more usual to allow different residual variances for each response, and
> often residual covariances between responses. rcov=~idh(trait):units fits
> different variances across responses (trait) but sets the covariances to
> zero. rcov= ~us(trait):units estimates the covariances too. There are other
> less general possibilities too (see CourseNotes) which may be usefull. For
> example rcov=~trait:units fits a common variance across responses and fixes
> the covariances to zero.
> Having random=~sid also assumes that each sid effect is identical across
> responses so you may want to relax this assumption as above.
> On 17 Jun 2010, at 13:43, Mike Lawrence wrote:
>> Hi folks,
>> I'm trying to do a mixed multinomial model with 3 response variables,
>> but I'm getting the error "please use idh() or us() error structure".
>> I believe that idh() and us() are used to specify the nature of the
>> interaction between multiple random variables, but I don't have
>> multiple random variables, so I'm not sure why I'm getting that error.
>> Some example data are below, but note that in my real data, the dv's
>> are indeed non-independent (they are in fact the number of trials
>> classified into each of three categories).
>> a = expand.grid(
>> sid = 1:20
>> , condition = factor(1:2)
>> a$group = factor(a$sid%%2)
>> a$sid = factor(a$sid)
>> a$dv1 = rnorm(nrow(a))
>> a$dv2 = rnorm(nrow(a))
>> a$dv3 = rnorm(nrow(a))
>> , random = ~ sid
>> , family = 'multinomial3'
>> , data = a
>> Mike Lawrence
>> Graduate Student
>> Department of Psychology
>> Dalhousie University
>> Looking to arrange a meeting? Check my public calendar:
>> ~ Certainty is folly... I think. ~
>> R-sig-mixed-models at r-project.org mailing list
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