[R] Bayesian Hidden Markov Models

Wed Feb 29 14:10:45 CET 2012

Dear James, 

The distances are normalized between zero and 1, so in your case all of them
will be zero. You can check that with

> res$Dist.for.model

And do 

> Q.NH(summary(res)[[1]]$beta, x=0)

To obtain the common transition matrix.

Cheers, 
Oscar 

On 29/2/12 03:59, "monkeylan" <lanjinchi at yahoo.com.cn> wrote:

> Dear Oscar,
>  
> I am extremely grateful to your help and detailed explanation of the use of
> RJaCGH package.
> But, when runing the sample codes you listed, another issue I am a little
> confused is as following:
> After runing summary(res), I have got the estimation of the random matrix
> Beta:
> 
> Parameters of the transition functions:
>        Normal  Gain
> Normal  0.000 4.258
> Gain    2.001 0.000
>  
> But, the transition probabilty matrix Q based on the aboving Beta is more
> concerned in my modeling.
> Here, I am not sure how can I get the  matrix Q. I did try the Q.NH
> functions.However, Shoud I set the distance parameter x be 1 or 0? I am not
> sure.
>  
>  If 1( according to my own understanding), the following result seems not
> reseanable.
>  
> tran<-matrix(c(0,2.001,4.528,0),2,2)
> Q.NH(beta=tran, x=1)
>      [,1] [,2]
> [1,]  0.5  0.5
> [2,]  0.5  0.5
>  
> Many thanks for your further help and time.
>  
> James Allan
> 
> --- 12年2月28日，周二, Oscar Rueda [via R]
> <ml-node+s789695n4427760h66 at n4.nabble.com> 写道：
> 
> 
> 发件人: Oscar Rueda [via R] <ml-node+s789695n4427760h66 at n4.nabble.com>
> 主题: Re: Bayesian Hidden Markov Models
> 收件人: "monkeylan" <lanjinchi at yahoo.com.cn>
> 日期: 2012年2月28日,周二,下午7:02
> 
> 
> Dear James,
> 
> Basically you just need the values (y) and the positions (in your case it
> would be the index of the times series). The chromosome argument does not
> apply to your case so it can be a vector of ones.
> If the positions are at the same distance between (equally spaced) then the
> model will be homogeneous.
> 
> So for example something like this would be enough:
>> library(RJaCGH)
>> y <- c(rnorm(100,0,1), rnorm(20, 2, 1), rnorm(50, 0, 1))
>> Pos <- 1:length(y)
>> Chrom <- rep(1, length(y))
>> res <- RJaCGH(y=y, Pos=Pos, Chrom=Chrom)
>> summary(res)
> 
> However, it uses a Reversible Jump algorithm and therefore jumps between
> models with different hidden states. I would suggest you take a look at the
> vignette that comes with the package or the paper that is referenced there
> for specific details of the model it fits.
> 
> 
> Hope it helps,
> Oscar
>  
> 
> 
> On 28/2/12 04:52, "monkeylan" <[hidden email]> wrote:
> 
> 
>> Dear Doctor Oscar,
>>  
>> Sorry for not noticing that you are the author of the RJaCGH package.
>> 
>> But I noticed that hidden Markov model in your package is with
>> non-homogeneous
>> transition probabilities. Here in my work, the HMM is just a first-order
>> homogeneous Markov chain, i.e. the  transition  matrix is constant.
>>  
>> So, Could you please tell me how can I adjust the R functions in your package
>> to implement my analysis?
>>  
>> Best Regards,
>>  
>> James Allan
>> 
>> 
>> --- 12年2月27日，周一, Oscar Rueda [via R]
>> <[hidden email]> 写道：
>> 
>> 
>> 发件人: Oscar Rueda [via R] <[hidden email]>
>> 主题: Re: Bayesian Hidden Markov Models
>> 收件人: "monkeylan" <[hidden email]>
>> 日期: 2012年2月27日,周一,下午6:05
>> 
>> 
>> Dear James,
>> Although designed for the analysis of copy number CGH microarrays, RJaCGH
>> uses a Bayesian HMM model.
>> 
>> Cheers,
>> Oscar
>> 
>> 
>> On 27/2/12 08:32, "monkeylan" <[hidden email]> wrote:
>> 
>> 
>>> Dear R buddies,
>>> 
>>> Recently, I attempt to model the US/RMB Exchange rate log-return time series
>>> with a *Hidden Markov model (first order Markov Chain & mixed Normal
>>> distributions). *
>>> 
>>> I have applied the RHmm package to accomplish this task, but the results are
>>> not so satisfying.
>>> So, I would like to try a *Bayesian method *for the parameter estimation of
>>> the Hidden Markov model.
>>> 
>>> Could anyone kindly tell me which R package can perform Bayesian estimation
>>> of the model?
>>> 
>>> Many thanks for your help and time.
>>> 
>>> Best Regards,
>>> James Allan
>>> 
>>> 
>>> --
>>> View this message in context:
>>> http://r.789695.n4.nabble.com/Bayesian-Hidden-Markov-Models-tp4423946p442394
>>> 6>>
> .
> 
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>>> Sent from the R help mailing list archive at Nabble.com.
>>> 
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>> Oscar M. Rueda, PhD.
>> Postdoctoral Research Fellow, Breast Cancer Functional Genomics.
>> Cancer Research UK Cambridge Research Institute.
>> Li Ka Shing Centre, Robinson Way.
>> Cambridge CB2 0RE
>> England
>> 
>> 
>> 
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> Oscar M. Rueda, PhD.
> Postdoctoral Research Fellow, Breast Cancer Functional Genomics.
> Cancer Research UK Cambridge Research Institute.
> Li Ka Shing Centre, Robinson Way.
> Cambridge CB2 0RE
> England
> 
> 
> 
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Oscar M. Rueda, PhD.
Postdoctoral Research Fellow, Breast Cancer Functional Genomics.
Cancer Research UK Cambridge Research Institute.
Li Ka Shing Centre, Robinson Way.
Cambridge CB2 0RE 
England 

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