jfa-package {jfa} | R Documentation |
jfa
is an R package for statistical audit sampling. The package provides functions for planning,
performing, evaluating, and reporting an audit sample. Specifically, these functions implement
standard audit sampling techniques for calculating sample sizes, selecting items from a population,
and evaluating the misstatement from a data sample or from summary statistics. Additionally,
the jfa
package allows the user to create a prior probability distribution to perform Bayesian
audit sampling using these functions.
The package and its intended workflow are also implemented with a graphical user interface in the Audit module of JASP, a free and open-source statistical software program.
For documentation on jfa
itself, including the manual and user guide
for the package, worked examples, and other
tutorial information visit the package website.
Below you can find several links to reference tables that contain statistical sample sizes, upper limits, and Bayes factors.
These tables are created using the planning()
and evaluation()
functions provided in the package. See the
corresponding help files for more information about these functions and how to replicate this output.
Sample sizes
Upper limits
One-sided p values
Bayes factors
Koen Derks (maintainer, author) | <k.derks@nyenrode.nl> |
Please use the citation provided by R when citing this package.
A BibTex entry is available from citation('jfa')
.
Useful links:
The cheat sheet for a quick overview of the intended workflow.
The vignettes for worked examples.
The issue page to submit a bug report or feature request.
# Load the jfa package
library(jfa)
# Load the BuildIt population
data('BuildIt')
############################################
### Example 1: Classical audit sampling ####
############################################
# Stage 1: Planning
stage1 <- planning(materiality = 0.03, expected = 0.01,
likelihood = 'poisson', conf.level = 0.95)
summary(stage1)
# Stage 2: Selection
stage2 <- selection(data = BuildIt, size = stage1,
units = 'values', values = 'bookValue',
method = 'interval', start = 1)
summary(stage2)
# Stage 3: Execution
sample <- stage2[['sample']]
# Stage 4: Evaluation
stage4 <- evaluation(materiality = 0.03, method = 'stringer',
conf.level = 0.95, data = sample,
values = 'bookValue', values.audit = 'auditValue')
summary(stage4)
######################################################################
### Example 2: Bayesian audit sampling using a non-informed prior ####
######################################################################
# Create the prior distribution
prior <- auditPrior(method = 'default', likelihood = 'poisson')
summary(prior)
# Stage 1: Planning
stage1 <- planning(materiality = 0.03, expected = 0.01,
likelihood = 'poisson', conf.level = 0.95, prior = prior)
summary(stage1)
# Stage 2: Selection
stage2 <- selection(data = BuildIt, size = stage1,
units = 'values', values = 'bookValue',
method = 'interval', start = 1)
summary(stage2)
# Stage 3: Execution
sample <- stage2[['sample']]
# Stage 4: Evaluation
stage4 <- evaluation(materiality = 0.03, conf.level = 0.95, data = sample,
values = 'bookValue', values.audit = 'auditValue',
prior = prior)
summary(stage4)
###################################################################
### Example 3: Bayesian audit sampling using an informed prior ####
###################################################################
# Create the prior distribution
prior <- auditPrior(method = 'arm', likelihood = 'poisson',
expected = 0.01, materiality = 0.03, cr = 0.6, ir = 1)
summary(prior)
# Stage 1: Planning
stage1 <- planning(materiality = 0.03, expected = 0.01,
likelihood = 'poisson', conf.level = 0.95, prior = prior)
summary(stage1)
# Stage 2: Selection
stage2 <- selection(data = BuildIt, size = stage1,
units = 'values', values = 'bookValue',
method = 'interval', start = 1)
summary(stage2)
# Stage 3: Execution
sample <- stage2[['sample']]
# Stage 4: Evaluation
stage4 <- evaluation(materiality = 0.03, conf.level = 0.95, data = sample,
values = 'bookValue', values.audit = 'auditValue',
prior = prior)
summary(stage4)