cellwise: Calculate Cellwise Flags for Anomaly Detection

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

Calculate Cellwise Flags for Anomaly Detection

Description

The function uses fuzzy logic 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 anomaly scores 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

cellwise(a, contamination = 0.08, epochs = 1000L)

Arguments

a	A long-format data.frame object with survey data. For details see information on the data format.
contamination	A number between zero and one used as a threshold when identifying outliers from the fuzzy scores. By default, the algorithm will identify 8% of the records as anomalies.
epochs	Number of epochs used to train a nontrivial robust linear model via the lion algorithm. By default, the algorithm will run 1000 iterations.

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.

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 indicators columns.

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 <- cellwise(toy[sample.int(100), ], 0.05, 10L)

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