bayesHRT: Calculate Cellwise Flags for Anomaly Detection Using Bayesian...

In HRTnomaly: Historical, Relational, and Tail Anomaly-Detection Algorithms

Calculate Cellwise Flags for Anomaly Detection Using Bayesian Testing

Description

The function uses the predictive posterior distribution based on emprical likelihoods to determine if a data entry is an outlier on not. The function takes a long-format data.frame object as input and returns it with two appended vectors. The first vector contains the posterior probabilities as numbers between zero and one, and the second vector provides a set of logical values indicating whether the data entry is an outlier (TRUE) or not (FALSE).

Usage

bayesHRT(a, prior = NULL)

Arguments

a	A long-format data.frame object with survey data. For details see information on the data format.
prior	A numerical value or vector of cell-level prior probabilites of observing an outlier. It is NULL by default. If false, the function searchers for a column named "prior" within the dataset. If such column is not provided in the dataset, a 0.5 non-informative value is used for all cells.

Details

The argument a is proivded as an object of class data.frame. This object is considered as a long-format data.frame, and it must have at least five columns with the following names:

"strata"

a character or factor column containing the information on the stratification.

"unit_id"

a character or factor column containing the ID of the statistical unit in the survey sample(x, size, replace = FALSE, prob = NULL).

"master_varname"

a character column containing the name of the observed variable.

"current_value_num"

a numeric the observed value, i.e., a data entrie

"pred_value"

a numeric a value observed on a previous survey for the same variable if available. If not available, the value can be set to NA or NaN. When working with longitudinal data, the value can be set to a time-series forecast or a filtered value.

"prior"

a numeric a value of prior probabilities of observign an outlier for the cell. If this column is omitted in the dataset provided, the function will use the values provided through the argument prior.

The data.frame object in input can have more columns, but the extra columns would be ignored in the analyses. However, these extra columns would be preserved in the system memory and returned along with the results from the cellwise outlier-detection analysis.

The use of the R-packages dplyr, purrr, and tidyr is highly recommended to simplify the conversion of datasets between long and wide formats.

Value

The long-format data.frame is provided as input data and contains extra columns i.e., anomaly flags and outlier posterior predictive distribution.

Author(s)

Luca Sartore [e-mail]

Examples

# Load the package
library(HRTnomaly)
set.seed(2025L)
# Load the 'toy' data
data(toy)
# Detect cellwise outliers
res <- bayesHRT(toy[sample.int(100), ], prior = 0.5)

HRTnomaly documentation built on April 3, 2025, 6:17 p.m.