cox.snell: Cox and Snell bound

In koenderks/auditR: A Multifunctional R Package for Auditing

Description

Calculates the Cox and Snell confidence bound for the maximum error in an audit population according to the methodology described by Cox & Snell (1979).

Usage

cox.snell(bookValues, auditValues, priorPi = 0.10, priorMu = 0.40, priorA = 1, priorB = 6,
           confidence = 0.95)

Arguments

bookValues	A vector of book values from sample.
auditValues	A vector of corresponding audit values from the sample.
priorPi	A value between 0 and 1 defining the prior expected error rate.
priorMu	A value between 0 and 1 defining the prior expected mean taint.
priorA	A value between 0 and 1 defining the prior expected successes.
priorB	A value between 0 and 1 defining the prior expected failures.
confidence	The amount of confidence desired from the bound (on a scale from 0 to 1), defaults to 95% confidence.

Value

An estimate of the mean taint per dollar unit in the population.

Details

EMPTY FOR NOW

Author(s)

Koen Derks, [e-mail]

References

Dworin, L., & Grimlund, R. A. (1986). Dollar-unit sampling: A comparison of the quasi-Bayesian and moment bounds. Accounting Review, 36-57.

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
cox.snell(bookValues,
          auditValues,
          confidence = 0.95)

koenderks/auditR documentation built on May 16, 2019, 7:16 p.m.