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@n4.nabble.com> 写道：


发件人: Oscar Rueda [via R] <ml-node+s789695n4427760h66@n4.nabble.com>
主题: Re: Bayesian Hidden Markov Models
收件人: "monkeylan" <lanjinchi@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-tp4423946p4423946>> 
. 

>> html 
>> Sent from the R help mailing list archive at Nabble.com. 
>> 
>> ______________________________________________ 
>> [hidden email] mailing list 
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code. 
> 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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> 
> 
> 
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> 
> If you reply to this email, your message will be added to the discussion 
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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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