rmixmvnorm: Random data generation from the mixture of multivariate...

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

Random data generation from the mixture of multivariate normals for hhsmm model

Description

Generates a vector of observations from mixture multivariate normal distribution in a specified state and using the parameters of a specified model

Usage

rmixmvnorm(j, model)

Arguments

j	a specified state
model	a hhsmmspec model

Value

a random vector of observations from mixture of multivariate normal distributions

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)
x = rmixmvnorm(1, model)

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