[R-sig-ME] MCMCglmm Poisson with an offset term and splines

[dani]

just a correction, the code prior$B$V[k,k]<-1e-8 works but the model provides this error


Error in MCMCglmm(y ~ x1 + x2 + X8 + x9 +x10 +  :
  prior list should contain elements R, G, and/or B only



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________________________________
From: Matthew <mew0099 at auburn.edu>
Sent: Friday, September 22, 2017 10:10 AM
To: dani; Jarrod Hadfield; r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] MCMCglmm Poisson with an offset term and splines

Hi Dani,

Jarrod probably meant

prior$B$mu[k]<-1 # assuming the offset term is last
prior$B$V[k,k]<-1e-8

Instead of:

prior$mu[k]<-1 # assuming the offset term is last
prior$B[k,k]<-1e-8


Sincerely,
Matthew

****************************************************
Matthew E. Wolak, Ph.D.
Assistant Professor
Department of Biological Sciences
Auburn University
306 Funchess Hall
Auburn, AL 36849, USA
Email: matthew.wolak at auburn.edu

On 22/09/17 12:01, dani wrote:
> Hi Jarrod,
>
>
> Thank you so much for your prompt and helpful response!
>
> I ran the code you sent me for the prior, but I am getting the following error:
>
> Error in prior$B[k, k] <- 1e-08 :
>    incorrect number of subscripts on matrix
>
>
> Here is what I get for prior:
>> prior
> $B
> $B$V
>         [,1]  [,2]  [,3]  [,4]  [,5]  [,6]  [,7]  [,8]  [,9] [,10] [,11]
>   [1,] 1e+08 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00
>   [2,] 0e+00 1e+08 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00
>   [3,] 0e+00 0e+00 1e+08 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00
>   [4,] 0e+00 0e+00 0e+00 1e+08 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00
>   [5,] 0e+00 0e+00 0e+00 0e+00 1e+08 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00
>   [6,] 0e+00 0e+00 0e+00 0e+00 0e+00 1e+08 0e+00 0e+00 0e+00 0e+00 0e+00
>   [7,] 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 1e+08 0e+00 0e+00 0e+00 0e+00
>   [8,] 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 1e+08 0e+00 0e+00 0e+00
>   [9,] 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 1e+08 0e+00 0e+00
> [10,] 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 1e+08 0e+00
> [11,] 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 0e+00 1e+08
>
> $B$mu
>   [1] 0 0 0 0 0 0 0 0 0 0 0
>
>
> $R
> $R$V
> [1] 1
>
> $R$nu
> [1] 0
>
>
> $G
> $G$G1
> $G$G1$V
> [1] 1
>
> $G$G1$nu
> [1] 0
>
>
> $G$G2
> $G$G2$V
> [1] 1
>
> $G$G2$nu
> [1] 0
>
>
> $G$G3
> $G$G3$V
> [1] 1
>
> $G$G3$nu
> [1] 0
>
>
>
> $mu
>   [1] NA NA NA NA NA NA NA NA NA NA  1
>
>
>
> I am not sure what to do next. Any help would be very appreciated!
>
> Best regards,
>
> N-M
>
>
>
> Sent from Outlook<http://aka.ms/weboutlook>
>
>
> ________________________________
> From: Jarrod Hadfield <j.hadfield at ed.ac.uk>
> Sent: Thursday, September 21, 2017 9:55 PM
> To: dani; r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] MCMCglmm Poisson with an offset term and splines
>
> Hi,
>
> The model looks OK as far as can be assessed without knowing the data.
> For the offset term you need to hold the associated coefficient at 1 by
> placing a strong prior on it. If  you want everything else to have the
> default prior then use:
>
> k<-11 # number of fixed effects
>
> prior<-list(B=list(V=diag(k)*1e8, mu=rep(0,k)),
>               R=list(V=1, nu=0),
>               G=list(G1=list(V=1, nu=0),
>                      G2=list(V=1, nu=0),
>                      G3=list(V=1, nu=0)))
>
> prior$mu[k]<-1 # assuming the offset term is last
> prior$B[k,k]<-1e-8
>
> The interpretation of the offset is simply the coefficient is assumed to
> be one and that the rate at which events occur is constant.
>
> Cheers,
>
> Jarrod
>
>
>
>
> On 22/09/2017 01:52, dani wrote:
>> Hello everyone,
>>
>> I have a Poisson model with an offset term that involves repeated observations nested into two cross-classified groups.
>>
>> The model includes
>> - four categorical variables
>> - 6 continuous variables (for one of them I would like to include a smoother)
>> - the offset=log(duration)
>>
>> I first used the spl2 function to create the fixed ((x6numspline1) and random terms (x6numspline2) for the smoother. I added the random smoother term to the other two random intercepts (for student ID and classroom) that I have (which are cross-classified).
>>
>> My question is: Do you find my model sound? Before I study the priors, I just wanted to run a default model - is my inclusion of an offset ok? Also, given that the observations are repeated and nested into both Student ID and classroom, I am not sure how to specify the variance structure in MCMCglmm (beginner here:))
>>
>> mc_spl0 <- MCMCglmm(number_events ~ x1cat+x2cat+x8cat+x9cat+x3num+x4num+x5num+x6numspline1+x7num+x8num+log(duration),
>>                       random =~ ID+class+idv(x6numspline2),
>>                       data   = newdat,
>>                       family = "poisson",
>>                       thin   = 100,
>>                       burnin = 10000,
>>                       nitt   = 150000,
>>                       saveX=TRUE, saveZ=TRUE, saveXL=TRUE, pr=TRUE, pl=TRUE)
>>
>> In addition, I am not sure what to make of the results for the offset term (included as a covariate in the model) in the output - how should I discuss them?
>>
>> Thank you all so much!
>> Best regards,
>> N-M.
>> <http://aka.ms/weboutlook>
>>
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>>
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>> R-sig-mixed-models at r-project.org mailing list
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>
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