R/rmb.R

In rankrate: Joint Statistical Models for Preference Learning with Rankings and Ratings

#' Random Mallows-Binomial generation
#'
#' This function randomly generates rankings and ratings from a Mallows-Binomial distribution.
#'
#' @param I A numeric entry indicating the number of observations to be drawn, i.e., the number of judges
#'   providing rankings and ratings.
#' @param p A vector specifying the underlying object qualities. All values between be between 0 and 1, inclusive.
#' @param pi0 A vector specifying the consensus (modal probability) ranking; should be used only for tie-breaking
#'   equal values in \code{p}.
#' @param theta A numeric entry specifying the Mallows scale parameter.
#' @param M A numeric entry specifying the maximum integer rating.
#' @param R A numeric entry specifying the length of the rankings to be drawn. When \code{R<=length(p)}, partial
#'   rankings are drawn by definition.
#'
#' @return A list containing elements \code{ratings}, a matrix of integer ratings with one row per judge
#'   and one column per object, \code{rankings}, and matrix of rankings (orderings) with one row per judge,
#'   and \code{M}, the inputted maximum integer rating.
#'
#' @examples
#' rmb(I=5,p=c(.1,.3,.4,.7,.9),theta=1,M=10)
#' rmb(I=10,p=c(.1,.3,.3,.7,.9),pi0=c(1,3,2,4,5),theta=5,M=40,R=3)
#'
#' @export

rmb <- function(I,p,theta,M,pi0=NULL,R=NULL){
  J <- length(p)
  if(is.null(pi0)){
    if(length(unique(p)) == J){pi0 <- order(p)
    }else{stop("Must specify unique p if pi0 is not supplied")}
  }

  ratings <- as.matrix(t(replicate(I,rbinom(n=J,size=M,prob=p))))
  rankings <- rmall(I,pi0,theta,R)

  return(list(ratings=ratings,rankings=rankings,M=M))
}