rohrbach.bound: Rohrbach's Augmented Variance Estimator Bound

In koenderks/auditR: A Multifunctional R Package for Auditing

Description

Calculates Rohrbach's (1993) augmented variance estimator confidence bound for the maximum error in an audit population.

Usage

rohrbach.bound(bookValues, auditValues, population.size, delta = 2.7, confidence = 0.95)

Arguments

bookValues	A vector of book values from sample.
auditValues	A vector of corresponding audit values from the sample.
population.size	An integer representing the size of the population.
delta	A value representing the adjustment factor for the variance. Rohrbach (1993) argued that the smallest value of delta that yields nominal coverage equals 2.7.
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

Rohrbach, K. J. (1993). Variance augmentation to achieve nominal coverage probability in sampling from audit populations. Auditing, 12(2), 79.

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
rohrbach.bound(bookValues = sample$bookValues,
               auditValues = sample$auditValues,
               population.size = 2400,
               confidence = 0.95)

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