Description Usage Arguments Value Author(s) See Also Examples

Calculates a binomial bound for a Monetary Unit Sampling evaluation.

Please treat as experimental.

1 | ```
MUS.binomial.bound(x, scope, as.pct, include.high.values, confidence.level)
``` |

`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%. |

Upper Error Limit calculed using the binomial bound.

Andre Guimaraes <[email protected]>

`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)
``` |

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

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