[R] 回复： Bayesian Hidden Markov Models
Dear Oscar,  Thanks for your help.It's so nice of you to explain this package to me.  Best Regards,  James LAN åä»¶äººï¼ Oscar Rueda [via R] æ¶ä»¶äººï¼ monkeylan åéæ¥æï¼ 2012å¹´2æ29æ¥, ææä¸, ä¸å 9:21 主é¢: Re: Bayesian Hidden Markov Models 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" <[hidden email]> 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 > > --- 12å¹´2æ28æ¥ï¼å¨äº, Oscar Rueda [via R] > <[hidden email]> åéï¼ > > > å件人: Oscar Rueda [via R] <[hidden email]> > 主é¢: Re: Bayesian Hidden Markov Models > æ¶ä»¶äºº: "monkeylan" <[hidden email]> > æ¥æ: 2012å¹´2æ28æ¥,å¨äº,ä¸å7:02 > > > 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 >> >> >> --- 12å¹´2æ27æ¥ï¼å¨ä¸, Oscar Rueda [via R] >> <[hidden email]> åéï¼ >> >> >> å件人: Oscar Rueda [via R] <[hidden email]> >> 主é¢: Re: Bayesian Hidden Markov Models >> æ¶ä»¶äºº: "monkeylan" <[hidden email]> >> æ¥æ: 2012å¹´2æ27æ¥,å¨ä¸,ä¸å6:05 >> >> >> 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 p
[R] 回复： Bayesian Hidden Markov Models
Dear Oscar,  I have used the the following codes to perform a Bayesian HMM for the exchange rate data. But, one intresting result is that the model fits a 6-state HMM with a common variance. This is very hard to understand. Because, from the plot graph, we could see there are obviously differents with high and low volatility.  So, could you please help me to take a look at this? Attached is the exchange rate data. I am really grateful for your help and time.  Best Regards,  James LAN   #input exchange rate data exrt<-read.table(file="exrt.txt",header=F) plot(exrt$V2) library(RJaCGH) y<-exrt$V2 Pos<- 1:length(y) Chrom <- rep(1, length(y)) res<-RJaCGH(y=y, Pos=Pos, Chrom=Chrom) summary(res) Q.NH(summary(res)[[1]]$beta, x=0) Summary for ARRAY array1: Distribution of the number of hidden states: 1 2 3 4 5 6 0 0 0 0 0 1 Model with 6 states: Distribution of the posterior means of hidden states:            10%   25%   50%   75%   90% Loss-1  -0.298 -0.284 -0.284 -0.279 -0.279 Loss-2  -0.144 -0.142 -0.142 -0.135 -0.135 Normal-1 -0.045 -0.043 -0.043 -0.040 -0.040 Normal-2 -0.004 -0.003 -0.003 0.000 0.000 Normal-3 0.047 0.056 0.056 0.059 0.059 Gain     0.177 0.197 0.197 0.198 0.198 Distribution of the posterior variances of hidden states:           10%  25%  50%  75%  90% Loss-1  0.001 0.001 0.001 0.001 0.001 Loss-2  0.001 0.001 0.001 0.001 0.001 Normal-1 0.001 0.001 0.001 0.001 0.001 Normal-2 0.001 0.001 0.001 0.001 0.001 Normal-3 0.001 0.001 0.001 0.001 0.001 Gain    0.001 0.001 0.001 0.001 0.001 Parameters of the transition functions:         Loss-1 Loss-2 Normal-1 Normal-2 Normal-3 Gain Loss-1   0.000 0.217   0.192   1.229   0.185 0.857 Loss-2   2.104 0.000   0.305   2.190   0.132 1.424 Normal-1 2.728 1.472   0.000   4.606   0.293 2.423 Normal-2 5.919 4.746   5.518   0.000   5.067 5.834 Normal-3 2.295 0.537   0.115   4.329   0.000 2.514 Gain     1.519 0.247   0.036   1.263   0.132 0.000 > Q.NH(summary(res)[[1]]$beta, x=0)              Loss-1     Loss-2   Normal-1   Normal-2   Normal-3 Loss-1  0.239381248 0.192598942 0.197535790 0.070058386 0.198853168 Loss-2  0.039503637 0.323847484 0.238632024 0.036241348 0.283843424 Normal-1 0.030559504 0.107234801 0.467453369 0.004669696 0.348627295 Normal-2 0.002624349 0.008474303 0.003915585 0.975979222 0.006151494 Normal-3 0.037727330 0.218834862 0.333794793 0.004936521 0.374412381 Gain    0.053064705 0.189481114 0.233947328 0.068592117 0.212423356                Gain Loss-1  0.101572465 Loss-2  0.077932083 Normal-1 0.041455335 Normal-2 0.002855048 Normal-3 0.030294113 Gain    0.242491380 åä»¶äººï¼ Oscar Rueda [via R] æ¶ä»¶äººï¼ monkeylan åéæ¥æï¼ 2012å¹´2æ29æ¥, ææä¸, ä¸å 9:21 主é¢: Re: Bayesian Hidden Markov Models 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" <[hidden email]> 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 > > --- 12å¹´2æ28æ¥ï¼å¨äº, Oscar Rueda [via R] > <[hidden email]> åéï¼ > > > å件人: Oscar Rueda [via R] <[hidden email]> > 主é¢: Re: Bayesian Hidden Markov Models > æ¶ä»¶äºº: "monkeylan" <[hidden email]> > æ¥æ: 2012å¹´2æ28æ¥,å¨äº,ä¸å7:02 > > > Dear James, > > Basically you just need the values (y) and the positions (i
[R] Bayesian Hidden Markov Models
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. __ R-help@r-project.org 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.
Re: [R] Bayesian Hidden Markov Models
Dear Oscar,
Â
I really appreciate your help for my problem. I have taken a look at the R
package RJaCGH you mentioned roughly, but I am really a little confused by the
CGH microarrays background of the package. Actually, I am a graduate student,
majoring Mathematical Statistics. So, I know nothing about the CGH microarrays.
Â
Have you ever used the RJaCGH package before? If so, could you please briefly
tell me how to use RJaCGH to implement a Bayesian Hidden Markov Models for my
univariate time series?
Â
Thanks again for your patience and time.
Â
Best Regards,
Â
James Allan
Â
--- 12å¹´2æ27æ¥ï¼å¨ä¸, Oscar Rueda [via R]
åéï¼
å件人: Oscar Rueda [via R]
主é¢: Re: Bayesian Hidden Markov Models
æ¶ä»¶äºº: "monkeylan"
æ¥æ: 2012å¹´2æ27æ¥,å¨ä¸,ä¸å6:05
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
NOTICE AND DISCLAIMER
This e-mail (including any attachments) is intended for ...{{dropped:16}}
__
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and provide commented, minimal, self-contained, reproducible code.
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To unsubscribe from Bayesian Hidden Markov Models, click here.
NAML
--
View this message in context:
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R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Bayesian Hidden Markov Models
Dear Doctor Oscar,
Â
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.
Â
So, Could you please tell me how can I adjust the R functions in your package
to implement my analysis?
Â
Best Regards,
Â
James Allan
--- 12å¹´2æ27æ¥ï¼å¨ä¸, Oscar Rueda [via R]
åéï¼
å件人: Oscar Rueda [via R]
主é¢: Re: Bayesian Hidden Markov Models
æ¶ä»¶äºº: "monkeylan"
æ¥æ: 2012å¹´2æ27æ¥,å¨ä¸,ä¸å6:05
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
NOTICE AND DISCLAIMER
This e-mail (including any attachments) is intended for ...{{dropped:16}}
__
[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.
If you reply to this email, your message will be added to the discussion
below:http://r.789695.n4.nabble.com/Bayesian-Hidden-Markov-Models-tp4423946p4424152.html
To unsubscribe from Bayesian Hidden Markov Models, click here.
NAML
--
View this message in context:
http://r.789695.n4.nabble.com/Bayesian-Hidden-Markov-Models-tp4423946p4427000.html
Sent from the R help mailing list archive at Nabble.com.
[[alternative HTML version deleted]]
__
R-help@r-project.org 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.
Re: [R] Bayesian Hidden Markov Models
Dear Oscar,
Â
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
Â
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.
Â
 If 1( according to my own understanding), the following result seems not
reseanable.
Â
tran<-matrix(c(0,2.001,4.528,0),2,2)
Q.NH(beta=tran, x=1)
    [,1] [,2]
[1,]Â 0.5Â 0.5
[2,]Â 0.5Â 0.5
Â
Many thanks for your further help and time.
Â
James Allan
--- 12å¹´2æ28æ¥ï¼å¨äº, Oscar Rueda [via R]
åéï¼
å件人: Oscar Rueda [via R]
主é¢: Re: Bayesian Hidden Markov Models
æ¶ä»¶äºº: "monkeylan"
æ¥æ: 2012å¹´2æ28æ¥,å¨äº,ä¸å7:02
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
Â
On 28/2/12 04:52, "monkeylan" <[hidden email]> wrote:
> Dear Doctor Oscar,
> Â
> 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.
> Â
> So, Could you please tell me how can I adjust the R functions in your package
> to implement my analysis?
> Â
> Best Regards,
> Â
> James Allan
>
>
> --- 12å¹´2æ27æ¥ï¼å¨ä¸, Oscar Rueda [via R]
> <[hidden email]> åéï¼
>
>
> å件人: Oscar Rueda [via R] <[hidden email]>
> 主é¢: Re: Bayesian Hidden Markov Models
> æ¶ä»¶äºº: "monkeylan" <[hidden email]>
> æ¥æ: 2012å¹´2æ27æ¥,å¨ä¸,ä¸å6:05
>
>
> 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
>
>
>
>
> NOTICE AND DISCLAIMER
> This e-mail (including any attachments) is intended for ...{{dropped:16}}
>
> __
