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" wrote:
> D
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:
Parameter
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
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 ma
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 no
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" wrote:
> Dear R buddies,
>
> Recently, I attempt to model the US/RMB Exchange rate log-return time series
> with a *Hidden Markov mode
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 t
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