[R-sig-ME] MCMCglmm prior distributions

[Jarrod Hadfield]

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.