#' Normal Approximation of the Maximum Error
#'
#' Calculates a confidence bound for the maximum error in an audit
#' population according to the Normal distribution.
#'
#' @usage normal.bound(bookValues, auditValues, confidence = 0.95)
#'
#' @param bookValues A vector of book values from sample.
#' @param auditValues A vector of corresponding audit values from the sample.
#' @param confidence The amount of confidence desired from the bound
#' (on a scale from 0 to 1), defaults to 95\% confidence.
#'
#' @return An estimate of the mean taint per dollar unit in the population.
#'
#' @section Details: EMPTY FOR NOW
#'
#' @author Koen Derks, \email{k.derks@nyenrode.nl}
#'
#' @seealso
#'
#' @references
#'
#' @examples
#' # Create an imaginary data set
#' bookValues <- rgamma(n = 2400, shape = 1, rate = 0.001)
#' error.rate <- 0.1
#' error <- sample(0:1, 2400, TRUE, c(1-error.rate, error.rate))
#' taint <- rchisq(n = 2400, df = 1) / 10
#' auditValues <- bookValues - (error * taint * bookValues)
#' frame <- data.frame( bookValues = round(bookValues,2),
#' auditValues = round(auditValues,2))
#' # Draw a sample
#' samp.probs <- frame$bookValues/sum(frame$bookValues)
#' sample.no <- sample(1:nrow(frame), 100, FALSE, samp.probs)
#' sample <- frame[sample.no, ]
#' # Calculate bound
#' normal.bound(bookValues = sample$bookValues,
#' auditValues = sample$auditValues,
#' confidence = 0.95)
#'
#' @keywords bound
#'
#' @export
normal.bound <- function(bookValues,
auditValues,
confidence = 0.95){
if(!(length(bookValues) == length(auditValues)))
stop("bookValues must be the same length as auditValues")
n <- length(auditValues)
t <- bookValues - auditValues
z <- t / bookValues
mu <- mean(z)
sigma <- sd(z)
se <- sigma / sqrt(n)
bound <- mu + qnorm(confidence, lower.tail = TRUE) * se
return(bound)
}
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