initial_estimate: initial estimation of the model parameters for a specified...

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

initial estimation of the model parameters for a specified emission distribution

Description

Provides the initial estimates of the model parameters of a specified emission distribution characterized by the mstep function, for an initial clustering obtained by initial_cluster

Usage

initial_estimate(clus, mstep = mixmvnorm_mstep, verbose = FALSE, ...)

Arguments

clus	an initial clustering obtained by initial_cluster
mstep	the mstep function of the EM algorithm with an style simillar to that of mixmvnorm_mstep
verbose	logical. if TRUE the outputs will be printed
...	additional parameters of the mstep function

Value

a list containing the following items:

• emission list the estimated parameterers of the emission distribution

• leng list of waiting times in each state for each sequence

• clusters the exact clusters of each observation (available if ltr=FALSE)

• nmix the number of mixture components (a vector of positive (non-zero) integers of length nstate)

• ltr logical. if TRUE a left to right hidden hybrid Markovian/semi-Markovianmodel is assumed

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)
par = initial_estimate(clus, verbose = TRUE)

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