Dear Maura,
On 12 Nov 2008, at 00:53, <mauede@alice.it> <mauede@alice.it> wrote:
> Thank you for your prompt answer.
> The breathing signal observations are the amplitude values as a
> function of time and phase.
> According to our model the hidden states are the different
> breathing types.
> Subjects, whose respiratiion process is regular, are likely to
> breathe, keeping the same cycle pattern/type,
> for many consecutive cycles. therefore dwelling in the same hidden
> state.
> The more regular the breathing process is, the more predictive its
> signal becomes the higher its amplitude autocorrelation order.
>
> I guess my question is: can msm implement an AutoRegressive HMM ?
depmixS4 can be used to fit markov mixtures of regression models. in
particular one could use
the previous observation as a predictor, if that is what you're
looking for ...
However, it seems that you are looking for an autoregressive regime
switching model.
Searching r-project.org gives quite a few hits on this that may be
helpful.
> It seems that depmixS4 can but it has a time series length
> constraint that I don't quite understand.
depmixS4 has no time series length constraints ...
best, Ingmar
> Thank you in advance for your attention.
>
> Kind regards,
> Maura Edelweiss
>
> -----Messaggio originale-----
> Da: Walter Zucchini [mailto:wzucchi@uni-goettingen.de]
> Inviato: mar 11/11/2008 11.32
> A: mauede@alice.it
> Oggetto: Re: R: Hidden Markov Models
>
> Dear Ms Monville,
>
> Hidden Markov models (HMMs), and that includes the msm implementation,
> are not based on the assumption that the observations are independent.
> Indeed HMMs are specifically designed to model serially dependent
> observations. Of course that doesn't mean that they can accommodate
> every type of serial dependence. It might turn out that HMMs are not
> useful for modelling whatever aspect of breathing you are
> investigating.
>
> HMMs are based on the assumption that the observations are
> "conditionally independent, given the states". This is a somewhat
> technical assumption that I won't try to explain by email, except
> to say
> that "conditional independence" does not imply independence of
the
> observations themselves.
>
> Regards,
>
> Walter Zucchini
>
> --
> Prof. Walter Zucchini,
> Institut fuer Statistik und Oekonometrie,
> Georg-August-Universitaet,
> Platz der Goettinger Sieben 5,
> 37073 Goettingen,
> Germany
> -----------------------------------------
> Tel +49-551-397286 FAX +49-551-397279
> ========================================>
>
> mauede@alice.it wrote:
>> Dear Prof. Zucchini,
>>
>> I am reading the comprehensive on-line documentation about msm.
>> The positive side is that it seems it has been designed for
>> biomedical statistics,
>> like Clinical Trials.
>> The bad side is that it does not seem to model observations
>> sequences that are not
>> independent but instead are autocorrelated, as it is my case. I
>> did not find any mention to
>> correlated observations therefore I assume the authors did not
>> have to face this problem.
>> Did I get it wrong ?
>>
>> Since the breathing signals amplitude is an autocorrelated
>> function of time and phase, I would
>> greatly appreciate your comments about the possibility to use msm
>> eventually after carring out
>> some modifications if the source code is available.
>>
>> Thank you in advance for your attention.
>>
>> Kind regards,
>> Maura Edelweiss
>>
>>
>> -----Messaggio originale-----
>> Da: Walter Zucchini [mailto:wzucchi@uni-goettingen.de]
>> Inviato: lun 20/10/2008 12.50
>> A: mauede@alice.it
>> Oggetto: Re: Hidden Markov Models
>>
>> Dear Ms Monville,
>>
>>> something in R that implements continuous HMMs
>>
>> The R-library "msm", "Multi-state Markov and hidden
Markov models in
>> continuous time", might do what you want.
>>
>> Regards,
>>
>> Walter Zucchini
>>
>>
>> --
>> Prof. Walter Zucchini,
>> Institut fuer Statistik und Oekonometrie,
>> Georg-August-Universitaet,
>> Platz der Goettinger Sieben 5,
>> 37073 Goettingen,
>> Germany
>> -----------------------------------------
>> Tel +49-551-397286 FAX +49-551-397279
>> ========================================>>
>>
>> mauede@alice.it wrote:
>>> Dear Prof. Zucchini,
>>>
>>> My name is Maura Edelweiss.
>>> I am a physicist (just graduated from Washington University) with
>>> a genuine interest in Statistical Signal Processing.
>>> Dr. Lamb and I are trying to build a model of human breathing
>>> from some breathing signals.
>>> SSA and some extra analysis (R and C++ code ) show that there are
>>> only a few breathing cycle types.
>>> That is, humans breathe switching from one cycle type to another.
>>> The breathing process seems to be well modeled by a Continuous
>>> output Density Hidden Markov Model.
>>> Since neither of us has previous experience with HMMs, we wonder
>>> if there is something in R that implements continuous HMMs and is
>>> reasonably well documented. That might make it easier to get
>>> started.
>>>
>>> Thank you in advance for your attention and help.
>>> Kind regards,
>>>
>>
>>
>>
>> Alice Messenger ;-) chatti anche con gli amici di Windows Live
>> Messenger e tutti i telefonini TIM!
>> Vai su http://maileservizi.alice.it/alice_messenger/index.html?
>> pmk=footer
>>
>
>
>
> Alice Messenger ;-) chatti anche con gli amici di Windows Live
> Messenger e tutti i telefonini TIM!
> Vai su http://maileservizi.alice.it/alice_messenger/index.html?
> pmk=footer
>
> [[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.
Ingmar Visser
Department of Psychology, University of Amsterdam
Roetersstraat 15
1018 WB Amsterdam
The Netherlands
t: +31-20-5256723
[[alternative HTML version deleted]]