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

Description Usage Arguments Value Author(s) See Also Examples

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

1
2
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 <[email protected]>

See Also

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

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
## 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.