[R-sig-ME] MCMCglmm prior distributions
Boby Mathew
bobyboby at gmail.com
Tue Oct 21 08:52:29 CEST 2014
Hello Jarrod Hadfield,
Below I have attached the sessionInfo(). So if beta's~ N(0,sigma^2) then
the G structure assign prior for sigma^2~Inv-Gamma(a,b). Is it right?.
Also is it possible to assign beta's some other distributions?
Thanks for the help.
regards,
Boby
R version 3.1.0 (2014-04-10)
Platform: x86_64-pc-linux-gnu (64-bit)
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] splines stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] INLA_0.0-1392038736 sp_1.0-15 MCMCglmm_2.21
[4] ape_3.1-4 coda_0.16-1 lattice_0.20-29
[7] Matrix_1.1-4
loaded via a namespace (and not attached):
[1] corpcor_1.6.7 grid_3.1.0 nlme_3.1-118 tcltk_3.1.0 tensorA_0.36
[6] tools_3.1.0
On Mon, Oct 20, 2014 at 7:03 PM, Jarrod Hadfield <j.hadfield at ed.ac.uk>
wrote:
> Hi,
>
> If the beta's are drawn from a normal distribution with the same variance
> then the idv(Z) model is appropriate. I thought you would get an error
> message with your code because Z was not in the data.frame. If this was not
> the case could you send me your sessionInfo()?
>
> Cheers,
>
> JArrod
>
>
>
>
>
>
>
>
> Quoting Boby Mathew <bobyboby at gmail.com> on Mon, 20 Oct 2014 17:09:52
> +0200:
>
> Hello Jarrod Hadfield,
>>
>> Thank you so much for your help. I have the winbugs code for estimation
>> of
>> my marker effect but the problem is winbugs is too slow and cannot handle
>> large datasets.
>>
>> my models is "y(observation) =Z%*%beta + noise"
>>
>> here 'Z is the markers matrix coded 0 and 1 and beta is marker effect I
>> used MCMCglmm for this model and included the marker matrix(Z) as random
>> (random=~idv(Z)).
>>
>> I simulated some data and MCMCglmm was giving good results when the number
>> of markers were less than the number of observation. Does MCMCglmm can
>> handle this type of models?
>>
>>
>>
>> I have attached the winbugs code here for the reference.
>>
>>
>> model{
>> for(i in 1:n){
>> y[i]~dnorm(mu[i],prec)
>> mu[i]<- inprod(x[i,], beta[])
>> }
>> for (j in 1: p){
>> beta[j]~dnorm(0,tau[j])
>> tau[j]<-1/var[j]
>> var[j]~dgamma(0.1,0.1)
>> }
>> sd~dunif(0,10)
>> sigma2<-sd*sd
>> prec<-1/sigma2
>>
>> }
>>
>> Once again thanks for the help
>>
>> regards,
>> Boby
>>
>> On Mon, Oct 20, 2014 at 4:17 PM, Jarrod Hadfield <j.hadfield at ed.ac.uk>
>> wrote:
>>
>> Hi Boby,
>>>
>>> If you mean a common t-prior (with estimated scale) for the random
>>> effects
>>> then you cannot. All that you can do is place a fixed t-prior on the
>>> `fixed' effects.
>>>
>>> Cheers,
>>>
>>> Jarrod
>>>
>>>
>>>
>>>
>>>
>>> Quoting Boby Mathew <bobyboby at gmail.com> on Mon, 20 Oct 2014 15:52:24
>>> +0200:
>>>
>>> Dear jarrod hadfield,
>>>
>>>>
>>>> How can I pace individual variances with a t-prior for the random
>>>> effects?
>>>> Could you please provide me an example?
>>>>
>>>> thanks for the help.
>>>>
>>>> regards,
>>>> Boby
>>>>
>>>> On Mon, Oct 20, 2014 at 2:58 PM, Jarrod Hadfield <j.hadfield at ed.ac.uk>
>>>> wrote:
>>>>
>>>> Hi,
>>>>
>>>>>
>>>>> This gives the b's a common variance. There is no point giving them
>>>>> individual variances unless you want to treat them as `fixed' but
>>>>> place a
>>>>> t-prior rather than a normal prior on each effect.
>>>>>
>>>>> Jarrod
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> Quoting Boby Mathew <bobyboby at gmail.com> on Mon, 20 Oct 2014 14:42:02
>>>>> +0200:
>>>>>
>>>>> Dear Jarrod Hadfield,
>>>>>
>>>>>
>>>>>> Here I have attached a small code with the simulation code. I want to
>>>>>> estimate the effect of 'b' here. As you suggested I treated fixed
>>>>>> effect
>>>>>> as
>>>>>> random and gave own variance. But I am not sure this is the right way.
>>>>>>
>>>>>> Could you please check whether the implementation is right?
>>>>>>
>>>>>> regards,
>>>>>> Boby
>>>>>>
>>>>>> mark=100; line=150
>>>>>>
>>>>>> x=round(matrix(runif(mark*line),nrow=mark))
>>>>>> b=rep(0,mark)
>>>>>> b[8]=3; b[80]=5;b[90]=5;
>>>>>>
>>>>>> noise=rnorm(line,0,sqrt(1))
>>>>>>
>>>>>>
>>>>>>
>>>>>> Line=1:line
>>>>>>
>>>>>> y = b%*%x + noise
>>>>>>
>>>>>> Z=t(x)
>>>>>>
>>>>>> library(MCMCglmm)
>>>>>>
>>>>>> data=data.frame(Phe=t(y),animal=Line)
>>>>>>
>>>>>> data$animal=as.factor(data$animal)
>>>>>>
>>>>>>
>>>>>> prior2.2 <- list(G = list(G1 = list(V = 1, n = 0.002)), R = list(V =
>>>>>> 1,
>>>>>> n
>>>>>> =
>>>>>> 0.002))
>>>>>>
>>>>>> mod_mcmc=MCMCglmm(Phe~1,random=~idv(Z),pr=T,data=data,
>>>>>> nitt=50000,thin=500,burnin=10000,prior=prior2.2)
>>>>>>
>>>>>> val=colMeans (mod_mcmc$Sol)
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Thu, Oct 16, 2014 at 5:51 PM, Jarrod Hadfield <j.hadfield at ed.ac.uk
>>>>>> >
>>>>>> wrote:
>>>>>>
>>>>>> Hi Boby,
>>>>>>
>>>>>>
>>>>>>> In short - no. I haven't tried this (or thought about it much), but
>>>>>>> you
>>>>>>> could treat each fixed effect as a single random effect with its own
>>>>>>> associated variance component. Presumably, you could then specify the
>>>>>>> prior
>>>>>>> for the variance component in a way that induces a prior
>>>>>>> t-distribution
>>>>>>> on
>>>>>>> the effect. Like the Laplace it has fatter tails than the Normal, but
>>>>>>> it
>>>>>>> lacks the peakiness and won't give some of the nice features of the
>>>>>>> LASSO.
>>>>>>>
>>>>>>> Cheers,
>>>>>>>
>>>>>>> Jarrod
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> Quoting Boby Mathew <bobyboby at gmail.com> on Thu, 16 Oct 2014
>>>>>>> 16:06:13
>>>>>>> +0200:
>>>>>>>
>>>>>>> Dear MCMCglmm users,
>>>>>>>
>>>>>>>
>>>>>>> Is it possible to use double exponential priors(Laplace) in
>>>>>>>> MCMCglmm?
>>>>>>>>
>>>>>>>> Thanks for the helps.
>>>>>>>>
>>>>>>>> regards,
>>>>>>>> Boby
>>>>>>>>
>>>>>>>>
>>>>>>>> --
>>>>>>>> Dr. Boby Mathew
>>>>>>>> INRES, University of Bonn
>>>>>>>> Katzenburgweg 5
>>>>>>>> Phone: 0228732031
>>>>>>>> 53115, Bonn,Germany.
>>>>>>>>
>>>>>>>> [[alternative HTML version deleted]]
>>>>>>>>
>>>>>>>> _______________________________________________
>>>>>>>> R-sig-mixed-models at r-project.org mailing list
>>>>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> --
>>>>>>> The University of Edinburgh is a charitable body, registered in
>>>>>>> Scotland, with registration number SC005336.
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> --
>>>>>> Dr. Boby Mathew
>>>>>> INRES, University of Bonn
>>>>>> Katzenburgweg 5
>>>>>> Phone: 0228732031
>>>>>> 53115, Bonn,Germany.
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>> --
>>>>> The University of Edinburgh is a charitable body, registered in
>>>>> Scotland, with registration number SC005336.
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>> --
>>>> Dr. Boby Mathew
>>>> INRES, University of Bonn
>>>> Katzenburgweg 5
>>>> Phone: 0228732031
>>>> 53115, Bonn,Germany.
>>>>
>>>>
>>>>
>>>
>>> --
>>> The University of Edinburgh is a charitable body, registered in
>>> Scotland, with registration number SC005336.
>>>
>>>
>>>
>>>
>>
>> --
>> Dr. Boby Mathew
>> INRES, University of Bonn
>> Katzenburgweg 5
>> Phone: 0228732031
>> 53115, Bonn,Germany.
>>
>>
>
>
> --
> The University of Edinburgh is a charitable body, registered in
> Scotland, with registration number SC005336.
>
>
>
--
Dr. Boby Mathew
INRES, University of Bonn
Katzenburgweg 5
Phone: 0228732031
53115, Bonn,Germany.
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