verifyModel {RcppHMM}

Model parameter verification

Description

Function used to verify that all the parameters satisfy the model constraints.

Usage

verifyModel(model)

Arguments

model

a list with the necessary parameters to set a hidden Markov model with fixed values.

Details

The model must have a stochastic transition matrix and a stochastic initial probability vector, also the row and column sizes must coincide with the number of provided state names. If the model uses categorical values, the emission matrix also must be stochastic and must have a column for each observation label and a row for each state name. And if the model uses discrete data, all the values must be positive assuming that they are counts.

Value

A "list" that contains the verified hidden Markov model parameters.

Examples

## Values for a hidden Markov model with categorical observations

n <- c("First","Second")
m <- c("A","T","C","G")
A <- matrix(c(0.378286,0.621714,
              0.830970,0.169030),
            nrow = 2,
            byrow = TRUE)

B <- matrix(c(0.1930795, 0.2753869, 0.3463100, 0.1852237,
              0.2871577, 0.1848870, 0.1614925, 0.3664628),
            nrow = 2,
            byrow = TRUE)

Pi <- c(0.4757797, 0.5242203)


params <- list( "Model" = "HMM",
                "StateNames" = n,
                "ObservationNames" = m,
                "A" = A,
                "B" = B,
                "Pi" = Pi)

verifiedModel <- verifyModel(params)
print(verifiedModel)

## Values for a hidden Markov model with discrete observations

n <- c("Low","Normal","High")

A <- matrix(c(0.5, 0.3,0.2,
              0.2, 0.6, 0.2,
              0.1, 0.3, 0.6),
            ncol=length(n), byrow=TRUE)

B <- c(2600,  # First distribution with mean 2600
       2700,  # Second distribution with mean 2700
       2800)  # Third distribution with mean 2800

Pi <- rep(1/length(n), length(n))

HMM.discrete <- verifyModel(list("Model"="PHMM", "StateNames" = n, "A" = A, "B" = B, "Pi" = Pi))
print(HMM.discrete)

## Values for a hidden Markov model with continuous observations                          
# Number of hidden states = 3
# Univariate gaussian mixture model

N = c("Low","Normal", "High")
A <- matrix(c(0.5, 0.3,0.2,
              0.2, 0.6, 0.2,
              0.1, 0.3, 0.6),
            ncol= length(N), byrow = TRUE)

Mu <- matrix(c(0, 50, 100), ncol = length(N))
Sigma <- array(c(144, 400, 100), dim = c(1,1,length(N)))
Pi <- rep(1/length(N), length(N))

newModel <- verifyModel(list( "Model"="GHMM", 
                              "StateNames" = N,
                              "A" = A, 
                              "Mu" = Mu, 
                              "Sigma" = Sigma, 
                              "Pi" = Pi))


## Values for a hidden Markov model with continuous observations                          
# Number of hidden states = 2
# Multivariate gaussian mixture model
# Observed vector with dimensionality of 3
N = c("X1","X2")
M <- 3

# Same number of dimensions
Sigma <- array(0, dim =c(M,M,length(N)))
Sigma[,,1] <- matrix(c(1.0,0.8,0.8,
                       0.8,1.0,0.8,
                       0.8,0.8,1.0), ncol = M)
Sigma[,,2] <- matrix(c(1.0,0.4,0.6,
                       0.4,1.0,0.8,
                       0.6,0.8,1.0), ncol = M)
Mu <- matrix(c(0, 5, 
               10, 0, 
               5, 10), nrow = M)

A <- matrix(c(0.6,0.3,
              0.4, 0.7), ncol = length(N))
Pi <- c(0.5, 0.5)

newModel <- verifyModel(list( "Model" = "GHMM",
                                "StateNames" = N,
                                "A" = A, 
                                "Mu" = Mu, 
                                "Sigma" = Sigma, 
                                "Pi" = Pi))