Dear R-Users, I am currently trying my luck with Hidden Markov Chains and use the package mhsmm. I was very "shocked" to see how the initial values for the transition matrix and probability distributions affected the outcomes. The results were extremely different even for small changes in the initial parameter settings. I would usually start with a "rough guesstimate" which seemed to work quite well:
data.hsmm <- list(x=as.numeric(data),N=nrow(data)) J <- 2 # 2 Regimes P0 <- matrix(c(0.99,0.01,0.03,0.97), nrow=J,ncol=J) b0 <- list(mu=c(0.05,-0.05),sigma=c(0.001,0.005)) # first regime has positive return and lwoer variance than second one; initiating values init0 <- c(0.5,0.5) # Initial starting probabilities startval <- hmmspec(init=init0, trans=P0, parms.emis=b0, dens.emis=dnorm.hsmm) tryCatch(regime <- hmmfit(data.hsmm, startval, mstep=mstep.norm,tol=1e-10),error=function(err) return(0)) Since it is a larger set of data (several different objects that are observed) the initial values need to be chosen "across all items to be evaluated". I did not see any difference when changing the tolerance or making any changes to the number of "maximal iterations". Any other package suggestions with which I may avoid this problem? How are you choosing the "set up variables"? Thank you very much in advance! Lui ______________________________________________ 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.
