hsmm {mhsmm} | R Documentation |
Estimates parameters of a HSMM using the EM algorithm.
hsmm(x, model, f, mstep, maxit = 100, sojourn.distribution = c("nonparametric","gamma","poisson") ,lock.transition = FALSE, lock.d = FALSE, M=NA,graphical=FALSE)
x |
A hsmm.data object (see Details) |
model |
Starting parameters for the model (see Details) |
f |
Density function of the emmission distribution |
mstep |
Re-estimates the parameters of density function on each iteration |
maxit |
Maximum number of iterations |
sojourn.distribution |
Waiting or sojourn distribution used (see Details) |
lock.transition |
If TRUE will not re-estimate the transition matrix |
lock.d |
If TRUE will not re-estimate the sojourn time density |
M |
Maximum number of time spent in a state (truncates the waiting distribution) |
graphical |
If TRUE will plot the sojourn densities on each iteration |
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 |
waiting |
A list of the parameters of the waiting distribution |
Jared O'Connell
Guedon, Y. (2003), Estimating hidden semi-Markov chains from discrete sequences, Journal of Computational and Graphical Statistics, Volume 12, Number 3, page 604-639 - 2003
hsmmspec, simulate.hsmmspec, predict.hsmm
J <- 3 init <- c(0,0,1) P <- matrix(c(0,.1,.4,.5,0,.6,.5,.9,0),nrow=J) B <- list(mu=c(10,15,20),sigma=c(2,1,1.5)) d <- list(lambda=c(10,30,60),shift=c(10,100,30),type='poisson') model <- hsmmspec(init,P,emission=B,sojourn=d,r=rnorm.hsmm) train <- simulate(model,nsim=100,seed=123456) plot(train,xlim=c(0,400)) start.poisson <- hsmmspec(init=rep(1/J,J),transition=matrix(c(0,.5,.5,.5,0,.5,.5,.5,0),nrow=J),emission=list(mu=c(4,12,23), sigma=c(1,1,1)),sojourn=list(lambda=c(9,25,40),shift=c(5,95,45))) M=500 h.poisson <- hsmm(train,start.poisson,f=dnorm.hsmm,mstep=mstep.norm,sojourn.distribution='poisson',M=M) plot(h.poisson$loglik,type='b',ylab='Log-likelihood',xlab='Iteration') ##has it converged? summary(h.poisson) predicted <- predict(h.poisson,train) table(train$s,predicted$s) ##classification matrix mean(predicted$s!=train$s) ##misclassification rate d <- cbind(dunif(1:M,0,50),dunif(1:M,100,175),dunif(1:M,50,130)) start.np <- hsmmspec(init=rep(1/J,J),transition=matrix(c(0,.5,.5,.5,0,.5,.5,.5,0),nrow=J),emission=list(mu=c(4,12,23), sigma=c(1,1,1)),sojourn=list(d=d)) h.np <- hsmm(train,start.np,f=dnorm.hsmm,mstep=mstep.norm,sojourn.distribution='nonparametric',M=M,graphical=TRUE) mean(predicted$s!=train$s) ##misclassification rate