Semi-Markov Model Fitting

dnonpar

R Documentation

pdf of the mixture of B-splines for hhsmm

Description

The probability density function of a mixture of B-splines for a specified observation vector, a specified state and a specified model's parameters

Usage

dnonpar(x, j, model, control = list(K = 5))

Arguments

`x`	an observation vector or matrix
`j`	a specified state between 1 to nstate
`model`	a hhsmmspec model
`control`	the parameters to control the density function. The simillar name is chosen with that of `nonpar_mstep`, to be used in `...` argument of the `hhsmmfit` function. Here, it contains only the parameter `K` which is the degrees of freedom for the B-spline, default is `K=5`

Value

the probability density function value

Author(s)

Morteza Amini, morteza.amini@ut.ac.ir, Reza Salehian, reza.salehian@ut.ac.ir

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)

mortamini/hhsmm documentation built on Aug. 27, 2022, 4:46 a.m.