initialize_model {hhsmm}

initialize the hhsmmspec model for a specified emission distribution

Description

Initialize the hhsmmspec model by using an initial clustering obtained by initial_cluster and the emission distribution characterized by mstep and dens.emission

Usage

initialize_model(
  clus,
  mstep = NULL,
  dens.emission = dmixmvnorm,
  sojourn = NULL,
  semi = NULL,
  M,
  verbose = FALSE,
  ...
)

Arguments

clus

initial clustering obtained by initial_cluster

mstep

the mstep function of the EM algorithm with an style simillar to that of mixmvnorm_mstep. If NULL, the mixmvnorm_mstep is considered for the complete data set and miss_mixmvnorm_mstep is considered for the data with missing values (NA or NaN)

dens.emission

the density of the emission distribution with an style simillar to that of dmixmvnorm

sojourn

one of the following cases:

• "nonparametric" non-parametric sojourn distribution

• "nbinom" negative binomial sojourn distribution

• "logarithmic" logarithmic sojourn distribution

• "poisson" poisson sojourn distribution

• "gamma" gamma sojourn distribution

• "weibull" weibull sojourn distribution

• "lnorm" log-normal sojourn distribution

• "auto" automatic determination of the sojourn distribution using the chi-square test

semi

logical and of one of the following forms:

• a logical value: if TRUE all states are considered as semi-Markovian else Markovian

• a logical vector of length nstate: the TRUE associated states are considered as semi-Markovian and FALSE associated states are considered as Markovian

• NULL if ltr=TRUE then semi = c(rep(TRUE,nstate-1),FALSE), else semi = rep(TRUE,nstate)

M

maximum number of waiting times in each state

verbose

logical. if TRUE the outputs will be printed the normal distributions will be estimated

...

additional parameters of the mstep function

Value

a hhsmmspec model containing the following items:

• init initial probabilities of states

• transition transition matrix

• parms.emission parameters of the mixture normal emission (mu, sigma, mix.p)

• sojourn list of sojourn time distribution parameters and its type

• dens.emission the emission probability density function

• mstep the M step function of the EM algorithm

• semi a logical vector of length nstate with the TRUE associated states are considered as semi-Markovian

Author(s)

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

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)
initmodel = initialize_model(clus = clus, sojourn = "gamma", 
M = max(train$N))