# 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.

## Usage

 `1` ``` 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 <alsguimaraes@gmail.com>

`MUS.evaluation` for evaluation of the audited sample.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```# 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) ```