Dear Maura, On 12 Nov 2008, at 00:53, <[EMAIL PROTECTED]> <[EMAIL PROTECTED]> 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:[EMAIL PROTECTED] > Inviato: mar 11/11/2008 11.32 > A: [EMAIL PROTECTED] > 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 > ========================================= > > > [EMAIL PROTECTED] 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:[EMAIL PROTECTED] >> Inviato: lun 20/10/2008 12.50 >> A: [EMAIL PROTECTED] >> 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 >> ========================================= >> >> >> [EMAIL PROTECTED] 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]] ______________________________________________ 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.
