createHmma: Create an asymmetric hidden Markov model.

In hmma: Constructs Asymmetric Hidden Markov Models

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

# 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)

hmma documentation built on July 2, 2020, 12:10 a.m.