MUS.combined.high.error.rate: Calculate a high error rate bound for a combined Monetary...
In MUS: Monetary Unit Sampling and Estimation Methods, Widely Used in Auditing

Description Usage Arguments Value Author(s) See Also Examples

Calculate a high error rate bound for a combined Monetary Unit Sampling evaluation.

Please treat as experimental.

1	MUS.combined.high.error.rate(evaluation, interval.type)

`evaluation`	A MUS.evaluation.result object used to calculate the combined bound.
`interval.type`	Optional. Interval type for high error rate evaluation. Default is "one-sided".

Upper Error Limit calculed using high error rate evaluation for a combined sample.

Andre Guimaraes <alsguimaraes@gmail.com>

MUS.evaluation for evaluation of the audited sample. MUS.combine for combining multiple evaluations.

# Assume 500 invoices, each between 1 and 1000 monetary units
data1 <- data.frame(book.value=round(runif(n=500, min=1, max=1000)))
# Plan a sample and cache it
plan1 <- MUS.planning(data=data1, tolerable.error=10000, expected.error=2000)
# Extract a sample and cache it (no high values exist in this example)
extract1 <- MUS.extraction(plan1)
# Copy book value into a new column audit values, and inject some error
audited1 <- extract1$sample$book.value*(1-rbinom(nrow(extract1$sample), 1, 0.05))
audited1 <- cbind(extract1$sample, audit.value=audited1)
# Evaluate the sample, cache and print it
evaluation1 <- MUS.evaluation(extract1, audited1)

# Assume 500 invoices, each between 1 and 1000 monetary units
data2 <- data.frame(book.value=round(runif(n=500, min=1, max=1000)))
# Plan a sample and cache it
plan2 <- MUS.planning(data=data2, tolerable.error=10000, expected.error=2000)
# Extract a sample and cache it (no high values exist in this example)
extract2 <- MUS.extraction(plan2)
# Copy book value into a new column audit values, and inject some error
audited2 <- extract2$sample$book.value*(1-rbinom(nrow(extract2$sample), 1, 0.05))
audited2 <- cbind(extract2$sample, audit.value=audited2)
# Evaluate the sample, cache and print it
evaluation2 <- MUS.evaluation(extract2, audited2)

combined <- MUS.combine(list(evaluation1, evaluation2))
MUS.combined.high.error.rate(combined)