1d:T7e1,# fit a hidden Markov model Estimates parameters of a HMM using the EM algorithm. ```r hmmfit(x,start.val,mstep=mstep.norm,lock.transition=FALSE,tol=1e-08,maxit=1000) ``` ## Arguments - `x`: A hsmm.data object (see Details) - `start.val`: Starting parameters for the model (see Details) - `mstep`: Re-estimates the parameters of density function on each iteration - `lock.transition`: If TRUE will not re-estimate the transition matrix - `maxit`: Maximum number of iterations - `tol`: Convergence tolerance ## Returns - **start**: A vector of the starting probabilities for each state - **a**: The transition matrix of the embedded Markov chain - **emission**: A list of the parameters of the emission distribution ## References Jared O'Connell, Soren Hojsgaard (2011). Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R., Journal of Statistical Software, 39(4), 1-22., URL http://www.jstatsoft.org/v39/i04/. Rabiner, L. (1989), A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, 77, 257-286. ## Author(s) Jared O'Connell jaredoconnell@gmail.com ## See Also `predict.hmm` ## Examples ```r J<-3 initial <- rep(1/J,J) P <- matrix(c(.8,.5,.1,0.05,.2,.5,.15,.3,.4),nrow=J) b <- list(mu=c(-3,0,2),sigma=c(2,1,.5)) model <- hmmspec(init=initial, trans=P, parms.emission=b,dens.emission=dnorm.hsmm) model train <- simulate(model, nsim=300, seed=1234, rand.emis=rnorm.hsmm) plot(train,xlim=c(0,100)) init0 <- rep(1/J,J) P0 <- matrix(1/J,nrow=J,ncol=J) b0 <- list(mu=c(-3,1,3),sigma=c(1,1,1)) startval <- hmmspec(init=init0, trans=P0,parms.emission=b0,dens.emission=dnorm.hsmm) h1 = hmmfit(train,startval,mstep=mstep.norm) plot(h1$loglik,type='b',ylab='Log-likelihood',xlab='Iteration') summary(h1) #proportion of incorrect states mean(train$s!=predict(h1,train)$s) #simulate a new test set test <- simulate(model, nsim=c(100,200,300), seed=1234,rand.emis=rnorm.hsmm) mean(test$s!=predict(h1,test)$s) ```

hmmfit function