R/limits.beta.r

In DanieLucas28/bcc: Beta Control Charts

#'@title Limits for discrete data
#'
#'
#'@description These function are used to compute the upper and lower control limits.
#'@param center sample/group center statistic.
#'@param std.dev within group standard deviation.
#'@param sizes sample sizes.
#'@param conf a numeric value used t o comput control limits, specifying the confidence level (0 < conf < 1)
#'@usage limits.beta(center, std.dev, sizes, conf)
#'@export limits.beta
#'@return The function \code{limits.beta} returns a matrix with lower and upper control limits.
#'
#'@examples
#'data(Montgomery2005)
#'limits.beta(center = 0.2313333, std.dev = 0.421685, sizes = Montgomery2005$Sample, conf = 0.9)



limits.beta <- function(center, std.dev, sizes, conf)
{
  probucl<-0.99865
  problcl<-0.00135
  sizes <- as.vector(sizes)
  n<-mean(sizes)
  alfa<-center*(((center*(1-center))/((center*(1-center))/n))-1) #=B5*(((B5*(1-B5))/((B5*(1-B5))/B4))-1)
  beta<-(1-center)*(((center*(1-center))/((center*(1-center))/n))-1) #=(1-B5)*(((B5*(1-B5))/((B5*(1-B5))/B4))-1)


  if (conf >= 1)
  {
    show("without application, determine another value for confidence.level")
  }
  else
  { if (conf > 0 & conf < 1)
  {
    lcl <-qbeta(problcl, alfa, beta)
    ucl <-qbeta(probucl, alfa, beta)
  }
    else stop("invalid conf argument. See help.")
  }
  limits <- matrix(c(lcl, ucl), ncol = 2)
  rownames(limits) <- rep("", length = nrow(limits))
  colnames(limits) <- c("LCL", "UCL")
  return(limits)
}