This function analyzes the frequency with which values get repeated within a set of numbers. Unlike Benford's law, and its generalizations, this approach examines the entire number at once, not only the first or last digit.
rv.test(x, check = 'last', method = 'af', B = 2000)a numeric vector of values from which the digits should be analyzed.
which digits to shuffle during the procedure. Can be last or lasttwo.
which property of the data is calculated. Defaults to af for average frequency, but can also be entropy for entropy.
how many samples to use in the bootstraping procedure.
An object of class dt.rv containing:
input data.
frequencies of observations in x.
vector of simulated samples.
counts for extracted integers.
counts for extracted decimals.
the number of observations in x.
the value the average frequency or entropy statistic.
the p-value for the test.
correlation test for the integer portions of the number versus the decimals portions of the number.
method used.
checked digits.
a character string giving the name(s) of the data.
To determine whether the data show an excessive amount of bunching, the null hypothesis that x does not contain an unexpected amount of repeated values is tested against the alternative hypothesis that x has more repeated values than expected. The statistic can either be the average frequency (\(AF = sum(f_i^2)/sum(f_i))\) of the data or the entropy (\(E = - sum(p_i * log(p_i))\), with \(p_i=f_i/n\)) of the data. Average frequency and entropy are highly correlated, but the average frequency is often more interpretable. For example, an average frequency of 2.5 means that, on average, your observations contain a value that appears 2.5 times in the data set.To quantify what is expected, this test requires the assumption that the integer portions of the numbers are not associated with their decimal portions.
Simohnsohn, U. (2019, May 25). Number-Bunching: A New Tool for Forensic Data Analysis. Retrieved from https://datacolada.org/77.