Description Usage Arguments Value Details Author(s) References See Also Examples
Calculates the Modified Stringer confidence bound for the maximum error in an audit population with the Pap and van Zuijlen (1992) adjustment.
1 | stringer.modified(bookValues, auditValues, confidence = 0.95)
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bookValues |
A vector of book values from sample. |
auditValues |
A vector of corresponding audit values from the sample. |
confidence |
The amount of confidence desired from the bound (on a scale from 0 to 1), defaults to 95% confidence. |
An estimate of the mean taint per dollar unit in the population
EMPTY FOR NOW
Koen Derks, k.derks@nyenrode.nl
Pap, G., & van Zuijlen, M. C. (1996). On the asymptotic behaviour of the Stringer bound 1. Statistica Neerlandica, 50(3), 367-389.
Stringer, K. W. (1963). Practical aspects of statistical sampling in auditing. In Proceedings of the Business and Economic Statistics Section (pp. 405-411). American Statistical Association.
stringer.bound
stringer.bickel
stringer.meikle
stringer.lta
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | # 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
stringer.modified(bookValues = sample$bookValues,
auditValues = sample$auditValues,
confidence = 0.95)
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