# dmixmvnorm: pdf of the mixture of multivariate normals for hhsmm In hhsmm: Hidden Hybrid Markov/Semi-Markov Model Fitting

## Description

The probability density function of a mixture multivariate normal for a specified observation vector, a specified state and a specified model's parameters

## Usage

 `1` ```dmixmvnorm(x, j, model) ```

## Arguments

 `x` an observation vector or matrix `j` a specified state between 1 to nstate `model` a hhsmmspec model

## Value

the probability density function value

## Author(s)

Morteza Amini, morteza.amini@ut.ac.ir, Afarin Bayat, aftbayat@gmail.com

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```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) p = dmixmvnorm(train\$x,1,model) ```

hhsmm documentation built on Jan. 10, 2022, 9:07 a.m.