R/dnonpar.R

Defines functions dnonpar

Documented in dnonpar

#' pdf of the mixture of B-splines for hhsmm
#'
#' The probability density function of a mixture of B-splines 
#' for a specified observation vector, a specified state and a specified 
#' model's parameters
#'
#' @author Morteza Amini, \email{morteza.amini@@ut.ac.ir}, 
#' Reza Salehian,  \email{reza.salehian@@ut.ac.ir}
#'
#' @param x an observation vector or matrix
#' @param j a specified state between 1 to nstate
#' @param model a hhsmmspec model
#' @param control the parameters to control the density function. 
#' The simillar name is chosen with that of \code{\link{nonpar_mstep}}, 
#' to be used in \code{...} argument of the \code{\link{hhsmmfit}} function.
#' Here, it contains only the parameter \code{K} which is the degrees of freedom for 
#' the B-spline, default is \code{K=5}
#'
#' @return the probability density function value
#'
#' @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)
#' clus = initial_cluster(train, nstate = 3, nmix = NULL, ltr = FALSE,
#' final.absorb = FALSE, verbose = TRUE)
#' semi <- c(FALSE, TRUE, FALSE)
#' initmodel = initialize_model(clus = clus, mstep = nonpar_mstep,
#' 	sojourn = "gamma", M = max(train$N), semi = semi)
#' p = dnonpar(train$x, 1, initmodel)
#'
#' @export
#'
dnonpar <- function(x, j, model, control = list(K = 5)){
	K = control$K
	x = as.matrix(x)
	d = ncol(x)
	coef = model$parms.emission$coef[[j]]
   	basis = btensor(lapply(1:d, function(i) x[, i]),
                   df = K, bknots = lapply(1:d, 
                      function(i) c(min(x[, i]) - 0.01,
                           max(x[, i]) + 0.01)))
	dens =    basis %*% coef
	dens
}

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hhsmm documentation built on May 30, 2022, 1:05 a.m.