MUS.planning: Plan a sample using Monetary Unit Sampling.

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

Description

Plan a sample for Monetary Unit Sampling. At the end of this planning step, you get to know the sample size.

Be aware that this MUS routines cannot calculate with decimals. Furthermore, you must provide book values etc. as Euro-Cent so that no decimals occur.

Usage

MUS.planning(data, col.name.book.values, confidence.level,
tolerable.error, expected.error, n.min, errors.as.pct, conservative, combined)

Arguments

data	A data frame or matrix which contains at least one column with the book values.
col.name.book.values	The name of the column that contains the book values. Default is "book.value".
confidence.level	The required confidence level. Default is 95%.
tolerable.error	The tolerable error (materiality) in Monetary Units.
expected.error	The expected error which is contained in the population in Monetary Units.
n.min	Minimum sample size that should be used. Default is 0.
errors.as.pct	Boolean. Tolerable and Expected error informed as percentages. Default is False.
conservative	Boolean. If true, use greater sample size between normal calculation and conservative algorithm (i.e., gamma-based, AICPA compatible).
combined	Boolean. Marks the dataset as a combination of multiple strata. Default is "FALSE".

Value

An object MUS.planning.result is returned which is a list containing the following elements:

data	For auditing acceptability and for further steps all inputs are also returned.
col.name.book.values	dito.
confidence.level	dito.
tolerable.error	dito.
expected.error	dito.
book.value	The calculated gross book value of the population. Negative values are ignored.
n	The calculated sample size based on the input parameters which is greater or egal than the provided minimum sample size.
High.value.threshold	Whenever a book value of an element is above the threshold, the element will be considered individually significant. Individual significant items will be audited completely, no sample extrapolation will be necessary.
tolerable.taintings	The number of taintings in the sample that will be acceptable at maximum.

Author(s)

Henning Prömpers <[e-mail]>

See Also

MUS.extraction for extraction of the planned sample and MUS.evaluation for evaluation of the extracted and audited sample.

Examples

## Simple Example
# Assume 500 invoices, each between 1 and 1000 monetary units
example.data.1 <- data.frame(book.value=round(runif(n=500, min=1,
max=1000)))
# Plan a sample and cache it
plan.results.simple <- MUS.planning(data=example.data.1,
tolerable.error=100000, expected.error=20000)

## Advanced Example
example.data.2 <- data.frame(own.name.of.book.values=round(runif(n=500,
min=1, max=1000)))
plan.results.advanced <- MUS.planning(data=example.data.2,
col.name.book.values="own.name.of.book.values", confidence.level=.70,
tolerable.error=100000, expected.error=20000, n.min=3)

Example output

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