Semi-Markov Model Fitting

make_model

R Documentation

make a hhsmmspec model for a specified emission distribution

Description

Provides a hhsmmspec model by using the parameters obtained by initial_estimate for the emission distribution characterized by mstep and dens.emission

Usage

make_model(
  par,
  mstep = mixmvnorm_mstep,
  dens.emission = dmixmvnorm,
  semi = NULL,
  M,
  sojourn
)

Arguments

`par`	the parameters obtained by `initial_estimate`
`mstep`	the mstep function of the EM algorithm with an style simillar to that of `mixmvnorm_mstep`
`dens.emission`	the density of the emission distribution with an style simillar to that of `dmixmvnorm`
`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
`sojourn`	the sojourn time distribution which is 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

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 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, morteza.amini@ut.ac.ir, Afarin Bayat, aftbayat@gmail.com

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)
par = initial_estimate(clus, verbose = TRUE)
model = make_model(par, semi = NULL, M = max(train$N), sojourn = "gamma")

mortamini/hhsmm documentation built on Aug. 27, 2022, 4:46 a.m.