# 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

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

## Usage

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

## Arguments

 `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".

## Value

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

## Author(s)

Andre Guimaraes <[email protected]>

`MUS.evaluation` for evaluation of the audited sample. `MUS.combine` for combining multiple evaluations.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26``` ```# 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) ```