Hi, I'm a newbie to the world of HMMs and HMMs in R. I've had a look at the hmm package and the RHmm package but I couldn't see anything straightforward on how a labelled sequential dataset with observed values and underlying states might be used to construct and train a HMM based on that data and no pre-computed values for the transition, emission or initial state distributions. Does anyone have any excerpts of code that could get me moving in the right direction?
To put it another way, lets say that I have the simple HMM topology that is shown here: http://en.wikipedia.org/wiki/File:HiddenMarkovModel.png And I have somehow collected datasets with observations and labelled hidden states: Sequence 1: Obs Hid y1 X1 y2 X2 y2 X2 y4 X1 ... ... y3 X3 ... Sequence N: Obs Hid y2 X1 y2 X2 y2 X1 y4 X1 ... ... y4 X1 I'm assuming categorial variables for y and x. I know I really am starting from from scratch here, so I'd be very grateful if anyone could point out to me how I could go about automatically constructing and parameterizing a HMM for data like this. Thanks for your patience. Claus. ______________________________________________ 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.
