Description Usage Arguments Value Details Author(s) References See Also Examples
Calculates the Stringer confidence bound for the maximum error in an audit population with the Leslie, Teitlebaum and Anderson (1979) adjustment for understatements.
1 | stringer.lta(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
Leslie, D. A., Teitlebaum, A. D., & Anderson, R. J. (1979). Dollar-unit sampling: a practical guide for auditors. Copp Clark Pitman; Belmont, Calif.: distributed by Fearon-Pitman.
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.modified
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.lta(bookValues = sample$bookValues,
auditValues = sample$auditValues,
confidence = 0.95)
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