# R/dnonpar.R In hhsmm: Hidden Hybrid Markov/Semi-Markov Model Fitting

#### 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)
n = nrow(x)
d = ncol(x)
coef = model\$parms.emission\$coef[[j]]
ll = lapply(1:d, function(i) bSpline(x[, i],
df = K, Boundary.knots = c(min(x[, i]) - 0.01, max(x[, i]) + 0.01)))
basis = ll[[1]]
if(d > 1){
for(jj in 2:d)
basis = sapply(1:n, function(i) outer(basis[i,],ll[[jj]][i,]))
basis = t(basis)
}
dens =    basis %*% coef
dens
}
```

## Try the hhsmm package in your browser

Any scripts or data that you put into this service are public.

hhsmm documentation built on May 29, 2024, 6:08 a.m.