R/mkv.R

In amvallone/estdaR: Exploratory Spatio-Temporal Data Analysis in R

#' @name mkv
#' @rdname mkv
#'
#' @title  Markov transition probability matrix
#' @description Compute thre Markov transition probability matrix
#'
#' @param m numerical matrix of n spatial unit ans t time periods
#' @param classes a number of a numeric vector of two or more unique cut points giving the number of intervals into which x will be cut
#' @param fixed logical, if it is TRUE the data are pooled over space and time and the quintiles calculated for the pooled data
#' @param type an integer between 1 and 9 selecting one of the nine quantile algorithms detailed below to be used. For more information see the quantile fuction
#' @param ... other argumnt to \code{\link{discret}} function
#' @return a list contaning the markov's trantiiona matrix and the markov's transition probability matrix
#'
#' @examples
#' data(us48)
#' data <- as.data.frame(us48)
#' pci <- data[,10:90]
#' rpci <- pci/matrix(1,dim(pci))%*%colMeans(pci)
#' m<-mkv(rpci)
#'
#' @export

mkv<-function(m,classes=5,fixed=FALSE,type=7,...){
	#Argument checks
  if(is.null(dim(m))==TRUE) stop("You must provide a matrix conteaining n spatial unita and t periods of time")
  t<-dim(m)[2]
  n<-dim(m)[1]
  if(t<2) stop("At least you must provide two period of time for this analysis")
  # pool or not cuts.
  if (fixed==TRUE){
    if(length(classes)!=1){
      if(max(classes)==1){
        cuts<-quantile(as.vector(m),classes,type = type, ...)
      }
    } else {
      cuts<-quantile(as.vector(m) ,seq(0,1,1/5),type = type, ...)
    }
    x<-apply(m,2,discret,classes=cuts)
  } else{
    x<-apply(m,2,discret,classes=classes)
  }
  mm<-mkv.int(x)
  #rownames(mm)<-clases
  #colnames(mm)<-clases
  mm.p <- mm/rowSums(mm)
  output <- list("Probabilities" = mm.p, "Transitions" = mm)
  return(output)
}