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 bickels's adjustment for less consevativeness.
1 | stringer.bickel(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
Bickel, P. J. (1992). Inference and auditing: the Stringer bound. International Statistical Review, 197-209.
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.lta
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.bickel(bookValues = sample$bookValues,
auditValues = sample$auditValues,
confidence = 0.95)
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