R/dmultinomial-hhsmm.R

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

#' pdf of the multinomial emission distribution for hhsmm
#'
#' The probability density function of a multinomial emission distribution
#' for a specified observation vector, a specified state and a specified 
#' model's parameters
#'
#' @author Morteza Amini, \email{morteza.amini@@ut.ac.ir}
#'
#' @param x the observation vector
#' @param j a specified state between 1 to nstate
#' @param model a hhsmmspec model
#' @param n the maximum possible level of the multinomial vector (i.e. from 1 to n) 
#'
#' @return the probability density function value
#'
#' @examples
#'J <- 2
#'initial <- c(1, 0)
#'semi <- rep(TRUE, 2)
#'P <- matrix(c(0, 1, 1, 0), 
#' nrow = J, byrow = TRUE)
#' par <- list(prob = list(c(0.6,  0.2, 0.2),
#'                            c(0.2, 0.6,  0.2)))
#' sojourn <- list(shape = c(1, 3), scale = c(2, 10), type = "gamma")
#' model <- hhsmmspec(init = initial, transition = P, parms.emis = par,
#' dens.emis = dmultinomial.hhsmm, remission = rmultinomial.hhsmm,
#'  mstep = mstep.multinomial,sojourn = sojourn, semi = semi)
#' train <- simulate(model, nsim = c(20, 30, 42, 50), seed = 1234, 
#' remission = rmultinomial.hhsmm)
#' clus = initial_cluster(train = train, nstate = 2, nmix = NULL,
#' ltr = FALSE, final.absorb = FALSE, verbose = TRUE)
#' initmodel = initialize_model(clus = clus, mstep = mstep.multinomial, n = 3,
#' dens.emission = dmultinomial.hhsmm, sojourn = "gamma", semi = rep(TRUE, 2), 
#' M = max(train$N),verbose = TRUE)
#' fit1 = hhsmmfit(x = train, model = initmodel, mstep = mstep.multinomial, n = 3, 
#' M = max(train$N))
#' homogeneity(fit1$yhat,train$s)
#'
#' @importFrom stats dmultinom
#'
#' @export
#'
dmultinomial.hhsmm <- function (x, j, model, n){ 
	y = t(sapply(1:n, function(i) 1*(x == i))) 
	sapply(1:ncol(y), function(i) dmultinom(y[,i], 1, model$parms.emission$prob[[j]]))
}