Dear Oscar, Thanks for your help.It's so nice of you to explain this package to me. Best Regards, James LAN 发件人： Oscar Rueda [via R] <ml-node+s789695n4431468h14@n4.nabble.com> 收件人： monkeylan <lanjinchi@yahoo.com.cn> 发送日期： 2012年2月29日, 星期三, 下午 9:21 主题: 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$Dist.for.modelAnd do> Q.NH(summary(res)[[1]]$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 > > --- 12年2月28日，周二, 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>> > . > >>> 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-tp4423946p4>>4>> 24152.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]] >> > 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 the above-named > person(s). If you are not the intended recipient, notify the sender > immediately, delete this email from your system and do not disclose or use for > any purpose. > > We may monitor all incoming and outgoing emails in line with current > legislation. We have taken steps to ensure that this email and attachments are > free from any virus, but it remains your responsibility to ensure that viruses > do not adversely affect you. > Cancer Research UK > Registered in England and Wales > Company Registered Number: 4325234. > Registered Charity Number: 1089464 and Scotland SC041666 > Registered Office Address: Angel Building, 407 St John Street, London EC1V > 4AD. > ______________________________________________ > [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-tp4423946p44 > 27760.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-tp4423946p4430489. > html > Sent from the R help mailing list archive at Nabble.com. > [[alternative HTML version deleted]] >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 the above-named person(s). If you are not the intended recipient, notify the sender immediately, delete this email from your system and do not disclose or use for any purpose. We may monitor all incoming and outgoing emails in line with current legislation. We have taken steps to ensure that this email and attachments are free from any virus, but it remains your responsibility to ensure that viruses do not adversely affect you. Cancer Research UK Registered in England and Wales Company Registered Number: 4325234. Registered Charity Number: 1089464 and Scotland SC041666 Registered Office Address: Angel Building, 407 St John Street, London EC1V 4AD. ______________________________________________ [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-tp4423946p4431468.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-tp4437957p4437957.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version deleted]]
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 发件人： Oscar Rueda [via R] <ml-node+s789695n4431468h14@n4.nabble.com> 收件人： monkeylan <lanjinchi@yahoo.com.cn> 发送日期： 2012年2月29日, 星期三, 下午 9:21 主题: 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$Dist.for.modelAnd do> Q.NH(summary(res)[[1]]$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 > > --- 12年2月28日，周二, 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>> > . > >>> 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-tp4423946p4>>4>> 24152.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]] >> > 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 the above-named > person(s). If you are not the intended recipient, notify the sender > immediately, delete this email from your system and do not disclose or use for > any purpose. > > We may monitor all incoming and outgoing emails in line with current > legislation. We have taken steps to ensure that this email and attachments are > free from any virus, but it remains your responsibility to ensure that viruses > do not adversely affect you. > Cancer Research UK > Registered in England and Wales > Company Registered Number: 4325234. > Registered Charity Number: 1089464 and Scotland SC041666 > Registered Office Address: Angel Building, 407 St John Street, London EC1V > 4AD. > ______________________________________________ > [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-tp4423946p44 > 27760.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-tp4423946p4430489. > html > Sent from the R help mailing list archive at Nabble.com. > [[alternative HTML version deleted]] >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 the above-named person(s). If you are not the intended recipient, notify the sender immediately, delete this email from your system and do not disclose or use for any purpose. We may monitor all incoming and outgoing emails in line with current legislation. We have taken steps to ensure that this email and attachments are free from any virus, but it remains your responsibility to ensure that viruses do not adversely affect you. Cancer Research UK Registered in England and Wales Company Registered Number: 4325234. Registered Charity Number: 1089464 and Scotland SC041666 Registered Office Address: Angel Building, 407 St John Street, London EC1V 4AD. ______________________________________________ [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-tp4423946p4431468.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-tp4440503p4440503.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version deleted]]
Dear james, There is an argument to RJaCGH() named var.equal that sets equal variances to each hidden state. The default is TRUE, so you might want to set it as FALSE. Cheers, Oscar On 3/3/12 02:20, "monkeylan" <lanjinchi at yahoo.com.cn> wrote:> 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 > > > ???? Oscar Rueda [via R] <ml-node+s789695n4431468h14 at n4.nabble.com> > ???? monkeylan <lanjinchi at yahoo.com.cn> > ????? 2012?2?29?, ???, ?? 9:21 > ??: 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 >> >> --- 12?2?28????, Oscar Rueda [via R] >> <[hidden email]> ??? >> >> >> ???: Oscar Rueda [via R] <[hidden email]> >> ??: Re: Bayesian Hidden Markov Models >> ???: "monkeylan" <[hidden email]> >> ??: 2012?2?28?,??,??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?2?27????, Oscar Rueda [via R] >>> <[hidden email]> ??? >>> >>> >>> ???: Oscar Rueda [via R] <[hidden email]> >>> ??: Re: Bayesian Hidden Markov Models >>> ???: "monkeylan" <[hidden email]> >>> ??: 2012?2?27?,??,??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-tp4423946p44239>>>> 4>>>> 6>> >> . >> >>>> 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-tp4423946p >>> 4>> > 4 >>> 24152.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]] >>> >> 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 the above-named >> person(s). If you are not the intended recipient, notify the sender >> immediately, delete this email from your system and do not disclose or use >> for >> any purpose. >> >> We may monitor all incoming and outgoing emails in line with current >> legislation. We have taken steps to ensure that this email and attachments >> are >> free from any virus, but it remains your responsibility to ensure that >> viruses >> do not adversely affect you. >> Cancer Research UK >> Registered in England and Wales >> Company Registered Number: 4325234. >> Registered Charity Number: 1089464 and Scotland SC041666 >> Registered Office Address: Angel Building, 407 St John Street, London EC1V >> 4AD. >> ______________________________________________ >> [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-tp4423946p4>> 4>> 27760.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-tp4423946p4430489>> .>> html >> Sent from the R help mailing list archive at Nabble.com. >> [[alternative HTML version deleted]] >> > 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 the above-named > person(s). If you are not the intended recipient, notify the sender > immediately, delete this email from your system and do not disclose or use for > any purpose. > > We may monitor all incoming and outgoing emails in line with current > legislation. We have taken steps to ensure that this email and attachments are > free from any virus, but it remains your responsibility to ensure that viruses > do not adversely affect you. > Cancer Research UK > Registered in England and Wales > Company Registered Number: 4325234. > Registered Charity Number: 1089464 and Scotland SC041666 > Registered Office Address: Angel Building, 407 St John Street, London EC1V > 4AD. > ______________________________________________ > [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-tp4423946p44 > 31468.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-tp4440503p4440503. > html > Sent from the R help mailing list archive at Nabble.com. > [[alternative HTML version deleted]] >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 the above-named person(s). If you are not the intended recipient, notify the sender immediately, delete this email from your system and do not disclose or use for any purpose. We may monitor all incoming and outgoing emails in line with current legislation. We have taken steps to ensure that this email and attachments are free from any virus, but it remains your responsibility to ensure that viruses do not adversely affect you. Cancer Research UK Registered in England and Wales Company Registered Number: 4325234. Registered Charity Number: 1089464 and Scotland SC041666 Registered Office Address: Angel Building, 407 St John Street, London EC1V 4AD.