hmma (version 1.1.0)

createHmma: Create an asymmetric hidden Markov model.

Description

The createHmma method creates a HMM-A from the specifications provided. The amount of states are implicitly derived from the initial distribution. Bayesian networks can be created using the bnlearn package.

Usage

createHmma(init, trans, bns)

Arguments

init

This initial distribution as a vector.

trans

The transition distribution as a matrix.

bns

The Bayesian networks for the states.

See Also

bnlearn for more information regarding the creation of Bayesian networks

Examples

# NOT RUN {
# Start by creating the initial and transition distribution
init <- c(0.3, 0.7)
trans <- c(0.4, 0.7, 0.6, 0.3)
dim(trans) <- c(2,2)

# Create a Baysian network for each state using 'bnlearn'
library(bnlearn)
struc <- model2network("[X1][X2]")
cptX1 <- matrix(c(0.15, 0.85), ncol = 2, dimnames = list(NULL, c("TRUE", "FALSE")))
cptX2 <- matrix(c(0.7, 0.3), ncol = 2, dimnames = list(NULL, c("TRUE", "FALSE")))

bn1 <- custom.fit(struc, dist = list(X1 = cptX1,
                                     X2 = cptX2))

struc <- model2network("[X2|X1][X1]")
cptX1 <- matrix(c(0.4, 0.6), ncol = 2, dimnames = list(NULL, c("TRUE", "FALSE")))
cptX2 <- matrix(c(0.9, 0.1, 0.5, 0.5), nrow = 2, ncol = 2)
dimnames(cptX2) <- list("X2" = c("TRUE", "FALSE"),
                        "X1" = c("TRUE", "FALSE"))

bn2 <- custom.fit(struc, dist = list(X1 = cptX1,
                                     X2 = cptX2))

bns <- list()
bns[[1]] <- bn1
bns[[2]] <- bn2

# Create the model
hmma <- createHmma(init = init, trans = trans, bns = bns)

# }