RcppHMM (version 1.0.1)

loglikelihood: Multiple observed sequences evaluation given a model

Description

This function computes the log-likelihood of an multiple observed sequences being generated by a hidden Markov model with fixed parameters.

Usage

loglikelihood(hmm, sequences)

Arguments

hmm

a list that contains all the necesary variables to define a hidden Markov model.

sequences

a matrix that contains in each row a sequence of observations to be evaluated.

Value

A value that represents the log likelihood of the multiple observed sequences given the hiddden Markov model.

References

Cited references are listed on the RcppHMM manual page.

See Also

generateObservations , verifyModel , evaluation

Examples

# NOT RUN {
# Set the model parameters
n <- c("First","Second")
m <- c("A","T","C","G")
A <- matrix(c(0.8,0.2,
              0.1,0.9),
            nrow = 2,
            byrow = TRUE)

B <- matrix(c(0.2, 0.2, 0.3, 0.3,
              0.4, 0.4, 0.1, 0.1),
            nrow = 2,
            byrow = TRUE)

Pi <- c(0.5, 0.5)

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

HMM <- verifyModel(params)

# Data simulation
set.seed(100)
length <- 100
seqs <- 100
observationSequences<- c()
for(i in 1:seqs){
  Y <- generateObservations(HMM , length)$Y
  observationSequences <- rbind(observationSequences , Y)
}

dim(observationSequences)
table(observationSequences)

#Sequences evaluation
loglikelihood(HMM, observationSequences)
# }