dmixmvnorm: pdf of the mixture of multivariate normals for hhsmm

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

pdf of the mixture of multivariate normals for hhsmm

Description

The probability density function of a mixture multivariate normal for a specified observation vector, a specified state and a specified model's parameters

Usage

dmixmvnorm(x, j, model)

Arguments

x	an observation vector or matrix
j	a specified state between 1 to nstate
model	a hhsmmspec model

Value

the probability density function value

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)
p = dmixmvnorm(train$x, 1, model)

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