hhsmmfit: hhsmm model fit

In hhsmm: Hidden Hybrid Markov/Semi-Markov Model Fitting

hhsmm model fit

Description

Fits a hidden hybrid Markov-semi-Markov model to a data of class "hhsmmdata" and using an initial model created by hhsmmspec or initialize_model

Usage

hhsmmfit(
  x,
  model,
  mstep = NULL,
  ...,
  M = NA,
  par = list(maxit = 100, lock.transition = FALSE, lock.d = FALSE, lock.init = FALSE,
    graphical = FALSE, verbose = TRUE)
)

Arguments

x	a data of class "hhsmmdata", which can also contain missing values (NA or NaN)
model	an initial model created by hhsmm.spec or initialize_model
mstep	the M step function for the EM algorithm, which also can be given in the model
...	additional parameters for the dens.emission and mstep functions
M	the maximum duration in each state
par	additional list of control parameters of the hhsmmfit function including the following items: maxit the maximum number of iterations for the EM algorithm lock.transition logical. if TRUE the transition matrix will not be updated through the EM algorithm lock.d logical. if TRUE the sojourn probability matrix d will not be updated through the EM algorithm lock.init logical. if TRUE the initial probability vector will not be updated through the EM algorithm graphical logical. if TRUE a plot of the sojourn probabilities will be plotted through the EM algorithm verbose logical. if TRUE the outputs will be printed

Value

a list of class "hhsmm" containing the following items:

• loglike the log-likelihood of the fitted model

• AIC the Akaike information criterion of the fitted model

• BIC the Bayesian information criterion of the fitted model

• model the fitted model

• estep_variables the E step (forward-backward) probabilities of the final iteration of the EM algorithm

• M the maximum duration in each state

• J the number of states

• NN the vector of sequence lengths

• f the emission probability density function

• mstep the M step function of the EM algorithm

• yhat the estimated sequence of states

Author(s)

Morteza Amini, [e-mail], Afarin Bayat, [e-mail]

References

Guedon, Y. (2005). Hidden hybrid Markov/semi-Markov chains. Computational statistics and Data analysis, 49(3), 663-688.

OConnell, J., & Hojsgaard, S. (2011). Hidden semi Markov models for multiple observation sequences: The mhsmm package for R. Journal of Statistical Software, 39(4), 1-22.

Examples

J <- 3
initial <- c(1, 0, 0)
semi <- c(FALSE, TRUE, FALSE)
P <- matrix(c(0.8, 0.1, 0.1, 0.5, 0, 0.5, 0.1, 0.2, 0.7), nrow = J, 
byrow = TRUE)
par <- list(mu = list(list(7, 8), list(10, 9, 11), list(12, 14)),
sigma = list(list(3.8, 4.9), list(4.3, 4.2, 5.4), list(4.5, 6.1)),
mix.p = list(c(0.3, 0.7), c(0.2, 0.3, 0.5), c(0.5, 0.5)))
sojourn <- list(shape = c(0, 3, 0), scale = c(0, 10, 0), type = "gamma")
model <- hhsmmspec(init = initial, transition = P, parms.emis = par,
dens.emis = dmixmvnorm, sojourn = sojourn, semi = semi)
train <- simulate(model, nsim = c(10, 8, 8, 18), seed = 1234, 
remission = rmixmvnorm)
clus = initial_cluster(train, nstate = 3, nmix = c(2 ,2, 2),ltr = FALSE,
final.absorb = FALSE, verbose = TRUE)
initmodel1 = initialize_model(clus = clus, sojourn = "gamma", 
M = max(train$N), semi = semi)
fit1 = hhsmmfit(x = train, model = initmodel1, M = max(train$N))

hhsmm documentation built on Aug. 8, 2023, 9:06 a.m.