MUS.binomial.bound: Calculate a binomial bound for a Monetary Unit Sampling...

In MUS: Monetary Unit Sampling and Estimation Methods, Widely Used in Auditing

Description

Calculates a binomial bound for a Monetary Unit Sampling evaluation.

Please treat as experimental.

Usage

    MUS.binomial.bound(x, scope, as.pct, include.high.values, confidence.level)

Arguments

x	A MUS.evaluation.result object (or a tainting vector) used to calculate the binomial bound.
scope	The required scope for the bound ("qty" or "value"). Default is "value".
as.pct	Boolean. Express results as percentage. Default is False.
include.high.values	Boolean. Whether the bound should include high values. Default is "TRUE".
confidence.level	The required confidence level. Default is 95%.

Value

Upper Error Limit calculed using the binomial bound.

Author(s)

Andre Guimaraes <[e-mail]>

See Also

MUS.evaluation for evaluation of the audited sample.

Examples

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

MUS documentation built on May 2, 2019, 12:36 p.m.