R/stateProbs.R

In moveHMM: Animal Movement Modelling using Hidden Markov Models

#' State probabilities
#'
#' For a given model, computes the probability of the process being in the different states
#' at each time point.
#'
#' @param m A \code{moveHMM} object.
#'
#' @return The matrix of state probabilities, with element [i,j] the probability
#' of being in state j in observation i.
#'
#' @examples
#' # m is a moveHMM object (as returned by fitHMM), automatically loaded with the package
#' m <- example$m
#'
#' sp <- stateProbs(m)
#'
#' @references
#' Zucchini, W. and MacDonald, I.L. 2009.
#' Hidden Markov Models for Time Series: An Introduction Using R.
#' Chapman & Hall (London).
#'
#' @export

stateProbs <- function(m)
{
    if(!is.moveHMM(m))
        stop("'m' must be a moveHMM object (as output by fitHMM)")

    data <- m$data
    nbStates <- ncol(m$mle$stepPar)
    nbAnimals <- length(unique(data$ID))

    if(nbStates==1)
        stop("No states to decode (nbStates=1)")

    nbObs <- nrow(data)
    la <- logAlpha(m) # forward log-probabilities
    lb <- logBeta(m) # backward log-probabilities
    stateProbs <- matrix(NA,nbObs,nbStates)

    aInd <- NULL
    for(i in 1:nbAnimals)
        aInd <- c(aInd,max(which(data$ID==unique(data$ID)[i])))

    for(i in nbObs:1){
        if(any(i==aInd)){
            c <- max(la[i,]) # cancels out below ; prevents numerical errors
            llk <- c + log(sum(exp(la[i,]-c)))
        }
        stateProbs[i,] <- exp(la[i,]+lb[i,]-llk)
    }

    return(stateProbs)
}