| Title: | Monetary Unit Sampling and Estimation Methods, Widely Used in Auditing |
|---|---|
| Description: | Sampling and evaluation methods to apply Monetary Unit Sampling (or in older literature Dollar Unit Sampling) during an audit of financial statements. |
| Authors: | Henning Prömpers, André Guimarães |
| Maintainer: | Henning Prömpers <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.6 |
| Built: | 2025-03-06 03:02:42 UTC |
| Source: | https://github.com/alsguimaraes/mus |
Sampling and evaluation methods to apply Monetary Unit Sampling (or in older literature Dollar Unit Sampling) during an audit of financial statements.
Monetary Unit Sampling (MUS), also known as Dollar Unit Sampling (DUS) or Probability-Proportional-to-Size Sampling (PPS), is a sampling approach that is widely used in auditing.
This package was written mainly for a research project. However, it should be possible to use the methods for practical auditing, too. Furthermore, the package comes with ABSOLUTELY NO WARRANTY. Use it at your own risk!
You have to walk through four steps: 1. Plan a sample and determine the sample size, use function: MUS.planning 2. Extract the sample, use function: MUS.extract 3. Audit the extracted sample (e.g. by asking for debtor confirmations). 4. Evaluate the audited sample, use function: MUS.evaluation
Henning Prömpers, André Guimarães Maintainer: Henning Prömpers <[email protected]>
MUS.planning for planning a sample,
MUS.extraction for extraction of the planned sample and
MUS.evaluation for evaluation of the extracted and
audited sample.
## Simple Example library(MUS) # 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) # Extract a sample and cache it (no high values exist in this example) extract.results.simple <- MUS.extraction(plan.results.simple) # Copy book values into a new column audit values audited.sample.simple <- extract.results.simple$sample audited.sample.simple <- cbind(audited.sample.simple, audit.value=audited.sample.simple$book.value) # Edit manually (if any audit difference occur) #audited.sample.simple <- edit(audited.sample.simple) # Evaluate the sample, cache and print it evaluation.results.simple <- MUS.evaluation(extract.results.simple, audited.sample.simple) print(evaluation.results.simple)## Simple Example library(MUS) # 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) # Extract a sample and cache it (no high values exist in this example) extract.results.simple <- MUS.extraction(plan.results.simple) # Copy book values into a new column audit values audited.sample.simple <- extract.results.simple$sample audited.sample.simple <- cbind(audited.sample.simple, audit.value=audited.sample.simple$book.value) # Edit manually (if any audit difference occur) #audited.sample.simple <- edit(audited.sample.simple) # Evaluate the sample, cache and print it evaluation.results.simple <- MUS.evaluation(extract.results.simple, audited.sample.simple) print(evaluation.results.simple)
Calculates a binomial bound for a Monetary Unit Sampling evaluation.
Please treat as experimental.
MUS.binomial.bound(x, scope, as.pct, include.high.values, confidence.level)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.
# 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)# 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)
Calculate a conservative sample size (AICPA, 2012). Based on Technical Notes on the AICPA Audit Guide Audit Sampling, Trevor Stewart, AICPA, 2012.
MUS.calc.n.conservative(confidence.level, tolerable.error, expected.error, book.value)MUS.calc.n.conservative(confidence.level, tolerable.error, expected.error, book.value)
confidence.level |
dito. |
tolerable.error |
Tolerable error in monetary units. |
expected.error |
Expected error in monetary units. |
book.value |
Book value in monetary units. |
Returns the (conservative) sample size.
Andre Guimaraes <[email protected]>
MUS.calc.n.conservative(0.95, 100000, 50000, 10000000)MUS.calc.n.conservative(0.95, 100000, 50000, 10000000)
Combine a list of MUS objects into a single object. Typical use case is to group multiple strata into a single object. Works with MUS.planning.result, MUS.extraction.result and MUS.evaluation.result objects.
MUS.combine(object.list)MUS.combine(object.list)
object.list |
A list of MUS.planning.result, MUS.extraction.result and MUS.evaluation.result objects. |
An object of the same type of the first item in the list is returned containing an aggregation of the objects in the list.
Andre Guimaraes <[email protected]>
## Simple Example # Assume 500 invoices, each between 1 and 1000 monetary units stratum.1 <- data.frame(book.value=round(runif(n=500, min=1, max=1000))) plan.1 <- MUS.planning(data=stratum.1, tolerable.error=100000, expected.error=20000) stratum.2 <- data.frame(book.value=round(runif(n=500, min=1, max=1000))) plan.2 <- MUS.planning(data=stratum.2, tolerable.error=100000, expected.error=20000) plan.combined <- MUS.combine(list(plan.1, plan.2)) print(plan.combined)## Simple Example # Assume 500 invoices, each between 1 and 1000 monetary units stratum.1 <- data.frame(book.value=round(runif(n=500, min=1, max=1000))) plan.1 <- MUS.planning(data=stratum.1, tolerable.error=100000, expected.error=20000) stratum.2 <- data.frame(book.value=round(runif(n=500, min=1, max=1000))) plan.2 <- MUS.planning(data=stratum.2, tolerable.error=100000, expected.error=20000) plan.combined <- MUS.combine(list(plan.1, plan.2)) print(plan.combined)
Calculate a high error rate bound for a combined Monetary Unit Sampling evaluation.
Please treat as experimental.
MUS.combined.high.error.rate(evaluation, interval.type)MUS.combined.high.error.rate(evaluation, interval.type)
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". |
Upper Error Limit calculed using high error rate evaluation for a combined sample.
Andre Guimaraes <[email protected]>
MUS.evaluation for evaluation of the audited sample.
MUS.combine for combining multiple evaluations.
# 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)# 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)
Evaluate a sample using Monetary Unit Sampling. At the end of the evaluation step, you get to know the audit conclusion for the population. To conduct the evaluation step it is required that you audited the sample and high values before. You can use print() for a comprehensive output.
MUS.evaluation(extract, filled.sample, filled.high.values, col.name.audit.values, col.name.riskweights, interval.type, print.advice, tainting.order, experimental, combined)MUS.evaluation(extract, filled.sample, filled.high.values, col.name.audit.values, col.name.riskweights, interval.type, print.advice, tainting.order, experimental, combined)
extract |
A MUS.extraction.result object that you got by executing the function MUS.extraction. |
filled.sample |
A data frame or matrix with the sample from the extraction routine that have an additional column with the audit values. |
filled.high.values |
A data frame or matrix with the high value items from the extraction routine that have an additional column with the audit values. |
col.name.audit.values |
Single character with the name of the column containing the audit value in filled.sample respectively filled.high.values. Default is "audit.value". |
col.name.riskweights |
Single character with the name of the column containing the risk weights in filled.sample respectively filled.high.values. Default is NULL, then no risk weights are included in the calcualations (the ordinary MUS case). |
interval.type |
Interval type for high error rate evaluation. Default is "one-sided". |
print.advice |
Boolean. Prints recommendations only if TRUE. Default is "TRUE". |
tainting.order |
Calculates UEL with different tainting orders (increasing, absolute, random). Default is "decreasing". |
experimental |
Boolean. Calculates other bounds, such as momentum, binomial, multinomial. Not ready for production. Default is "FALSE". |
combined |
Boolean. Marks the dataset as a combination of multiple strata. Default is "FALSE". |
An object MUS.evaluation.result is returned which is a list containing the following elements:
MUS.extraction.result elements |
All elements that are contained in MUS.extraction.result object. For auditing acceptability and for further steps all inputs are also returned. |
filled.sample |
dito. |
filled.high.values |
dito. |
col.name.audit.values |
dito. |
Overstatements.Result.Details |
Detail table for overstatements found in the sample. |
Understatements.Result.Details |
Detail table for understatements found in the sample. |
Results.Sample |
Comprehensive results of sample evaluation. |
Results.High.values |
Comprehensive results of individually significant item evaluation. |
Results.Total |
Comprehensive results of both evaluations (sample and individual significant items). |
acceptable |
Boolean, if population is acceptable given results, confidence level and materiality. |
Henning Prömpers <[email protected]>
## 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) # Extract a sample and cache it (no high values exist in this example) extract.results.simple <- MUS.extraction(plan.results.simple) # Copy book value into a new column audit values audited.sample.simple <- extract.results.simple$sample audited.sample.simple <- cbind(audited.sample.simple, audit.value=audited.sample.simple$book.value) # Edit manually (if any audit difference occur) #audited.sample.simple <- edit(audited.sample.simple) # Evaluate the sample, cache and print it evaluation.results.simple <- MUS.evaluation(extract.results.simple, audited.sample.simple) print(evaluation.results.simple) ## 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) extract.results.advanced <- MUS.extraction(plan.results.advanced, start.point=5, seed=1, obey.n.as.min=TRUE) extract.results.advanced <- MUS.extraction(plan.results.advanced) audited.sample.advanced <- extract.results.advanced$sample audited.sample.advanced <- cbind(audited.sample.advanced, own.name.of.audit.values=audited.sample.advanced$own.name.of.book.values) #audited.sample.advanced <- edit(audited.sample.advanced) evaluation.results.advanced <- MUS.evaluation(extract.results.advanced, audited.sample.advanced, col.name.audit.values="own.name.of.audit.values") print(evaluation.results.advanced)## 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) # Extract a sample and cache it (no high values exist in this example) extract.results.simple <- MUS.extraction(plan.results.simple) # Copy book value into a new column audit values audited.sample.simple <- extract.results.simple$sample audited.sample.simple <- cbind(audited.sample.simple, audit.value=audited.sample.simple$book.value) # Edit manually (if any audit difference occur) #audited.sample.simple <- edit(audited.sample.simple) # Evaluate the sample, cache and print it evaluation.results.simple <- MUS.evaluation(extract.results.simple, audited.sample.simple) print(evaluation.results.simple) ## 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) extract.results.advanced <- MUS.extraction(plan.results.advanced, start.point=5, seed=1, obey.n.as.min=TRUE) extract.results.advanced <- MUS.extraction(plan.results.advanced) audited.sample.advanced <- extract.results.advanced$sample audited.sample.advanced <- cbind(audited.sample.advanced, own.name.of.audit.values=audited.sample.advanced$own.name.of.book.values) #audited.sample.advanced <- edit(audited.sample.advanced) evaluation.results.advanced <- MUS.evaluation(extract.results.advanced, audited.sample.advanced, col.name.audit.values="own.name.of.audit.values") print(evaluation.results.advanced)
Extends a sample that requires further evidence. Works with MUS.extraction.result.
Please treat as experimental.
MUS.extend(extract, new_plan=NULL, additional.n=NULL)MUS.extend(extract, new_plan=NULL, additional.n=NULL)
extract |
An object of the type MUS.extraction.result to be extended. |
new_plan |
Provide a new MUS plan. If null, you must provide the qty of items to extend the sample. |
additional.n |
Ignored if new_plan is provided, otherwise sample will be extended by additional.n items |
Returns an extended MUS.extraction.result object.
Andre Guimaraes <[email protected]>
## Simple Example # Assume 500 invoices mydata <- data.frame(book.value= round(c(runif(n=480, min=10,max=20000), runif(n=20, min=15000,max=50000))) ) # Plan a sample and cache it plan <- MUS.planning(data=mydata, tolerable.error=50000, expected.error=3000) # Extract a sample and cache it extract <- MUS.extraction(plan, obey.n.as.min=TRUE) # Create a new plan new_plan <- MUS.planning(data=mydata, tolerable.error=50000, expected.error=5000) # extends the sample using the new plan extended <- MUS.extend(extract, new_plan) # extends the sample by 20 itens using the original plan extended20 <- MUS.extend(extract, additional.n=20)## Simple Example # Assume 500 invoices mydata <- data.frame(book.value= round(c(runif(n=480, min=10,max=20000), runif(n=20, min=15000,max=50000))) ) # Plan a sample and cache it plan <- MUS.planning(data=mydata, tolerable.error=50000, expected.error=3000) # Extract a sample and cache it extract <- MUS.extraction(plan, obey.n.as.min=TRUE) # Create a new plan new_plan <- MUS.planning(data=mydata, tolerable.error=50000, expected.error=5000) # extends the sample using the new plan extended <- MUS.extend(extract, new_plan) # extends the sample by 20 itens using the original plan extended20 <- MUS.extend(extract, additional.n=20)
Extract a sample using Monetary Unit Sampling. At the end of the extraction step, you get to know the items that you have to audit.
MUS.extraction(plan, start.point, seed, obey.n.as.min, combined)MUS.extraction(plan, start.point, seed, obey.n.as.min, combined)
plan |
A MUS.planning.result object that you got by executing the function MUS.planning. |
start.point |
The extraction method uses fixed interval sampling. The monetary unit specified by start.point will be drawn in each interval. Default is NULL, in this case a random number is drawn. |
seed |
A seed number which will be used to initialise the random number generator. Default is NULL which means that no new random number generator is initialised. This argument is mainly used for simulations or if you want to be able to regenerate the sample on another computer. |
obey.n.as.min |
Boolean. If set to TRUE, the sample interval will be exactly recalculated and thus the sample size will be exactly the planned sample size. Default is FALSE which is what most commercial statistical software do. In this case the drawn sample size might be slightly smaller than specified. |
combined |
Boolean. Marks the dataset as a combination of multiple strata. Default is "FALSE". |
An object MUS.extraction.result is returned which is a list containing the following elements:
MUS.planning.result elements |
All elements that are contained in MUS.planning.result object. For auditing acceptability and for further steps all inputs are also returned. |
start.point |
dito. |
seed |
dito. |
obey.n.as.min |
dito. |
high.values |
The part of the population that is classified as individually significant items. All of them have to be audited. |
sample.population |
The part of the population that is not in the high-values-subpopulation. |
sampling.interval |
The reassessed sampling interval that have to be used for evaluation. |
sample |
The extracted sample. All elements have to be audited. |
Henning Prömpers <[email protected]>
MUS.planning for planning a sample and
MUS.evaluation for evaluation of the extracted and
audited sample.
## 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) # Extract a sample and cache it extract.results.simple <- MUS.extraction(plan.results.simple) ## 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) extract.results.advanced <- MUS.extraction(plan.results.advanced, start.point=5, seed=0, obey.n.as.min=TRUE)## 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) # Extract a sample and cache it extract.results.simple <- MUS.extraction(plan.results.simple) ## 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) extract.results.advanced <- MUS.extraction(plan.results.advanced, start.point=5, seed=0, obey.n.as.min=TRUE)
Calculate MUS Factor (AICPA, 2012). Based on Technical Notes on the AICPA Audit Guide Audit Sampling, Trevor Stewart, AICPA, 2012.
MUS.factor(confidence.level, pct.ratio)MUS.factor(confidence.level, pct.ratio)
confidence.level |
dito. |
pct.ratio |
Expected.error by tolerable.error. |
Returns the MUS factor.
Andre Guimaraes <[email protected]>
MUS.factor(0.95, 0.5)MUS.factor(0.95, 0.5)
Calculates the moment bound (Dworkin & Grimlund, 1984) for a Monetary Unit Sampling evaluation.
Please treat as experimental.
MUS.moment.bound(x, confidence.level, as.pct, include.high.values)MUS.moment.bound(x, confidence.level, as.pct, include.high.values)
x |
A MUS.evaluation.result object (or a tainting vector) used to calculate the moment bound. |
confidence.level |
The required confidence level. Default is 95%. |
as.pct |
Boolean. Express results as percentage. Default is False. |
include.high.values |
Boolean. Whether the bound should include high values. Default is "TRUE". |
Upper Error Limit calculed using the moment bound.
Andre Guimaraes <[email protected]>
MUS.evaluation for evaluation of the audited sample.
sample = c(rep(0, 96), -.16, .04, .18, .47) MUS.moment.bound(sample) # 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.moment.bound(evaluation)sample = c(rep(0, 96), -.16, .04, .18, .47) MUS.moment.bound(sample) # 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.moment.bound(evaluation)
Calculates a multinomial bound for a Monetary Unit Sampling evaluation.
Please treat as experimental.
MUS.multinomial.bound(x, as.pct, include.high.values)MUS.multinomial.bound(x, as.pct, include.high.values)
x |
A MUS.evaluation.result object used to calculate the multinomial bound. |
as.pct |
Boolean. Express results as percentage. Default is False. |
include.high.values |
Boolean. Whether the bound should include high values. Default is "TRUE". |
Upper Error Limit calculed using the multinomial bound.
Andre Guimaraes <[email protected]>
MUS.evaluation for evaluation of the audited sample.
# 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.multinomial.bound(evaluation)# 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.multinomial.bound(evaluation)
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.
MUS.planning(data, col.name.book.values, confidence.level, tolerable.error, expected.error, n.min, errors.as.pct, conservative, combined)MUS.planning(data, col.name.book.values, confidence.level, tolerable.error, expected.error, n.min, errors.as.pct, conservative, combined)
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". |
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. |
Henning Prömpers <[email protected]>
MUS.extraction for extraction of the planned sample and
MUS.evaluation for evaluation of the extracted and
audited sample.
## 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)## 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)
Pretty and comprehensive printing of MUS evaluation results that can be used for working papers.
## S3 method for class 'MUS.evaluation.result' print(x, error.rate, print.misstatements, print.planning, print.extraction, print.error.as.pct, print.advice, style, use.pander, ...)## S3 method for class 'MUS.evaluation.result' print(x, error.rate, print.misstatements, print.planning, print.extraction, print.error.as.pct, print.advice, style, use.pander, ...)
x |
A MUS.evaluation.result object that you got by executing the function MUS.evaluation. |
error.rate |
Selects type of error rate calculation (i.e., "high", "low", "both" or "auto"). Defaults to "auto". |
print.misstatements |
Boolean. Should misstatements table be printed? Defaults to TRUE. |
print.planning |
Boolean. Should planning parameters be printed? Defaults to FALSE. |
print.extraction |
Boolean. Should extraction parameters be printed? Defaults to FALSE. |
print.error.as.pct |
Boolean. Should errors as percentage be printed? Defaults to TRUE. |
print.advice |
Boolean. Should recommendations be printed? Defaults to TRUE. |
style |
Two options: "report" or "default". Report uses an alternative layout. Defaults to "default". |
use.pander |
Boolean. Uses pander to generate rmarkdown report. Defaults to FALSE. |
... |
Further arguments, currently ignored. |
Henning Prömpers <[email protected]>
MUS.evaluation for evaluation of the extracted and
audited sample.
Pretty and comprehensive printing of MUS extraction results that can be used for working papers.
## S3 method for class 'MUS.extraction.result' print(x, print.title, print.planning, style, use.pander, ...)## S3 method for class 'MUS.extraction.result' print(x, print.title, print.planning, style, use.pander, ...)
x |
A MUS.evaluation.result object that you got by executing the function MUS.evaluation. |
print.title |
Boolean. Should title be printed? Defaults to TRUE. |
print.planning |
Boolean. Should planning parameters be printed? Defaults to FALSE. |
style |
Two options: "report" or "default". Report uses an alternative layout. Defaults to "default". |
use.pander |
Boolean. Uses pander to generate rmarkdown report. Defaults to FALSE. |
... |
Further arguments, currently ignored. |
Henning Prömpers <[email protected]>
MUS.extraction for extraction of the audit sample.
Pretty and comprehensive printing of MUS planning results that can be used for working papers.
## S3 method for class 'MUS.planning.result' print(x, print.title, style, use.pander, ...)## S3 method for class 'MUS.planning.result' print(x, print.title, style, use.pander, ...)
x |
A MUS.evaluation.result object that you got by executing the function MUS.evaluation. |
print.title |
Boolean. Should title be printed? Defaults to TRUE. |
style |
Two options: "report" or "default". Report uses an alternative layout. Defaults to "default". |
use.pander |
Boolean. Uses pander to generate rmarkdown report. Defaults to FALSE. |
... |
Further arguments, currently ignored. |
Henning Prömpers <[email protected]>
MUS.planning for planning of the audit sample.