# dnonpar: pdf of the mixture of B-splines for hhsmm In hhsmm: Hidden Hybrid Markov/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)

```

hhsmm documentation built on March 18, 2022, 5:16 p.m.