HRTnomaly
Description
Enhanced Anomaly Detection for Historical, Relational, and Tail Cellwise Outlier
Details
| Package: | HRTnomaly |
| Type: | Package |
| License: | AGPL-3 |
The presence of outliers in a dataset can substantially bias the results of statistical analyses. To correct for outliers, micro edits are manually performed on all records. A set of constraints and decision rules is typically used to aid the editing process. However, straightforward decision rules might overlook anomalies arising from disruption of linear relationships. This package provides a computationally efficient method to identify historical, tail, and relational anomalies at the data-entry level. A score statistic is developed for each anomaly type, using a distribution-free approach motivated by the Bienaymé-Chebyshev's inequality, and fuzzy logic is used to detect cellwise outliers resulting from different types of anomalies. Each data entry is individually scored and individual scores are combined into a final score to determine anomalous entries. The HRTnomaly package has proven to be a powerful tool for identifying outliers that are not easily detectable using other traditional methods.
For a complete list of exported functions, use library(help = "HRTnomaly").
This research was supported by the U.S. Department of Agriculture, National Agriculture Statistics Service. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy.
Author(s)
Luca Sartore, Lu Chen, Justin van Wart, Andrew Dau, and Valbona Bejleri
Maintainer: Luca Sartore [email protected]
References
Agostinelli C, Leung A, Yohai VJ, Zamar RH (2015). Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination. Test, 24(3): 441–461.
Alqallaf F, Van Aelst S, Yohai VJ, Zamar RH (2009). Propagation of outliers in multivariate data. The Annals of Statistics, 311–331.
Bienaymé IJ (1867). Considérations à l'appui de la découverte de Laplace sur la loi de probabilité dans la méthode des moindres carrés. Journal de Mathématiques Pures et Appliquées, 2(12): 158–176.
Chepulis MA, Shevlyakov G (2020). On outlier detection with the Chebyshev type inequalities. Journal of the Belarusian State University. Mathematics and Informatics, 3: 28–35.
Filzmoser P, Gregorich M (2020). Multivariate outlier detection in applied data analysis: Global, local, compositional and cellwise outliers. Mathematical Geosciences, 52(8): 1049–1066.
Gupta MM, Qi J (1991). Theory of t-norms and fuzzy inference methods. Fuzzy Sets and Systems, 40(3): 431–450.
Huber PJ, Ronchetti EM (1981). Robust statistics. John Wiley & Sons, New York.
O'Gorman TJ (1994). The effect of cosmic rays on the soft error rate of a DRAM at ground level. IEEE Transactions on Electron Devices, 41(4): 553–557.
Rousseeuw PJ, Van den Bossche W (2018). Detecting deviating data cells. Technometrics, 60(2): 135–145.
Sandqvist AP (2016). Identifizierung von Ausreissern in eindimensionalen gewichteten Umfragedaten. KOF Analysen, 2016(2): 45–56.
Sartore L, Chen L, Bejleri V (2024). Empirical Inferences Under Bayesian Framework to Identify Cellwise Outliers. Stats, 7: 1244–1258.
Sartore L, Chen L, van Wart J, Dau A, Bejleri V (2024). Identifying Anomalous Data Entries in Repeated Surveys. Journal of Data Science, 22(3): 436–455.
Tchebichef P (1867). Des valeurs moyennes. Journal de Mathématiques Pures et Appliquées, 2(12): 177–184.
Examples
# Load the package library(HRTnomaly) set.seed(2025L) # Load the 'toy' data data(toy) # Detect cellwise outliers using robust regression res_c <- cellwise(toy[sample.int(33), ], epochs = 10L) # Detect cellwise outliers using Bayesian testing res_g <- bayesHRT(toy[sample.int(33), ], prior = 0.5) # Detect record level outliers using Deep Isolation Forests res_t <- dif(iris)# Load the package library(HRTnomaly) set.seed(2025L) # Load the 'toy' data data(toy) # Detect cellwise outliers using robust regression res_c <- cellwise(toy[sample.int(33), ], epochs = 10L) # Detect cellwise outliers using Bayesian testing res_g <- bayesHRT(toy[sample.int(33), ], prior = 0.5) # Detect record level outliers using Deep Isolation Forests res_t <- dif(iris)
Calculate Cellwise Flags for Anomaly Detection Using Bayesian Testing
Description
The function uses the predictive posterior distribution based on empirical likelihoods to determine if a data entry is an outlier or 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)bayesHRT(a, prior = NULL)
Arguments
a |
A long-format |
prior |
A numerical value or vector of cell-level prior probabilities of observing an outlier. It is |
Details
The argument a is provided 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
characterorfactorcolumn containing the information on the stratification. "unit_id"-
a
characterorfactorcolumn containing the ID of the statistical unit in the survey sample(x, size, replace = FALSE, prob = NULL). "master_varname"-
a
charactercolumn containing the name of the observed variable. "current_value_num"-
a
numericthe observed value, i.e., a data entry "pred_value"-
a
numerica value observed on a previous survey for the same variable if available. If not available, the value can be set toNAorNaN. When working with longitudinal data, the value can be set to a time-series forecast or a filtered value. "prior"-
a
numerica value of prior probabilities of observing an outlier for the cell. If this column is omitted in the dataset provided, the function will use the values provided through the argumentprior.
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
A data frame with the same columns as the input data frame, plus the following additional columns:
- post_prob
-
The posterior probability of the value being an outlier.
- outlier
-
A boolean indicating whether the value is an outlier.
- anomaly_flag
-
A character string indicating the type of anomaly detected, if any.
Author(s)
Luca Sartore [email protected]
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)# 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)
Calculate Cellwise Flags for Anomaly Detection Using Robust Bayesian Methods
Description
The function uses a Bayesian approach to determine if a data entry is an outlier or 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 for a cell to be anomalous, and the second vector provides
a set of logical values indicating whether the data entry is an outlier (TRUE) or not (FALSE).
Usage
bayeswise(a, prior = NULL, epochs = 1000L, weighted = NULL, ...)bayeswise(a, prior = NULL, epochs = 1000L, weighted = NULL, ...)
Arguments
a |
A long-format |
prior |
A numerical value or vector of cell-level prior probabilities of observing an outlier. It is |
epochs |
Number of epochs used to train a nontrivial robust linear model via the lion algorithm. By default, the algorithm will run 1000 iterations. |
weighted |
A string indicating the isolation forest algorithm ( |
... |
Additional arguments that are passed to the functions executing the isolation algorihtms (or not used if |
Details
The argument a is provided 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
characterorfactorcolumn containing the information on the stratification. "unit_id"-
a
characterorfactorcolumn containing the ID of the statistical unit in the survey sample(x, size, replace = FALSE, prob = NULL). "master_varname"-
a
charactercolumn containing the name of the observed variable. "current_value_num"-
a
numericthe observed value, i.e., a data entry "pred_value"-
a
numerica value observed on a previous survey for the same variable if available. If not available, the value can be set toNAorNaN. When working with longitudinal data, the value can be set to a time-series forecast or a filtered value. "prior"-
a
numerica value of prior probabilities of observing an outlier for the cell. If this column is omitted in the dataset provided, the function will use the values provided through the argumentprior.
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
A data frame with the same columns as the input data frame, plus the following additional columns:
- prior
-
The prior probability used for the cell (either input or derived).
- zScore
-
The z-score of the cell.
- hScore
-
The h-score of the cell.
- rScore
-
The r-score of the cell.
- tScore
-
The t-score of the cell.
- score
-
The final outlier score of the cell, representing the posterior probability of being anomalous.
- outlier
-
A boolean indicating whether the cell is an outlier.
- anomaly_flag
-
A character string indicating the type of anomaly detected, if any.
Author(s)
Luca Sartore [email protected]
Examples
# Load the package library(HRTnomaly) set.seed(2025L) # Load the 'toy' data data(toy) # Detect cellwise outliers using Bayesian Analysis res <- bayeswise(toy[sample.int(100), ], 0.5, 10L)# Load the package library(HRTnomaly) set.seed(2025L) # Load the 'toy' data data(toy) # Detect cellwise outliers using Bayesian Analysis res <- bayeswise(toy[sample.int(100), ], 0.5, 10L)
Calculate Cellwise Flags for Anomaly Detection Using Bayesian Bootstrap
Description
The function uses Bayesian bootstrap to determine if a data entry is an outlier or not.
The function takes a long-format data.frame object as input and returns it with appended vectors.
The output includes flags for different quantiles and means of the contamination threshold distribution.
Usage
bootHRT(a, contamination = 0.08, boot_max_it = 1000L)bootHRT(a, contamination = 0.08, boot_max_it = 1000L)
Arguments
a |
A long-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 approximately 8% of the data entries as anomalies. |
boot_max_it |
An integer number determining the iterations performed by Bayesian bootstrap algorithm. It is set to |
Details
The argument a is provided 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
characterorfactorcolumn containing the information on the stratification. "unit_id"-
a
characterorfactorcolumn containing the ID of the statistical unit in the survey sample(x, size, replace = FALSE, prob = NULL). "master_varname"-
a
charactercolumn containing the name of the observed variable. "current_value_num"-
a
numericthe observed value, i.e., a data entry "pred_value"-
a
numerica value observed on a previous survey for the same variable if available. If not available, the value can be set toNAorNaN. 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
A data frame with the same columns as the input data frame, plus the following additional columns:
- score
-
The raw anomaly score for each cell.
- outlier_1qt
-
A boolean indicating if the cell is an outlier based on the first quantile threshold.
- outlier_2qt
-
A boolean indicating if the cell is an outlier based on the second quantile (median) threshold.
- outlier_3qt
-
A boolean indicating if the cell is an outlier based on the third quantile threshold.
- outlier_1mn
-
A boolean indicating if the cell is an outlier based on the mean threshold minus one standard deviation.
- outlier_2mn
-
A boolean indicating if the cell is an outlier based on the mean threshold.
- outlier_3mn
-
A boolean indicating if the cell is an outlier based on the mean threshold plus one standard deviation.
- anomaly_flag
-
A character string indicating the type of anomaly detected, if any (e.g., "h", "t", "r").
The returned object also includes an attribute "thresholds" which is a numeric vector of length boot_max_it containing samples from the posterior distribution of the contamination threshold.
Author(s)
Luca Sartore [email protected]
Examples
# Load the package library(HRTnomaly) set.seed(2025L) # Load the 'toy' data data(toy) # Detect cellwise outliers res <- bootHRT(toy, boot_max_it = 10)# Load the package library(HRTnomaly) set.seed(2025L) # Load the 'toy' data data(toy) # Detect cellwise outliers res <- bootHRT(toy, boot_max_it = 10)
Calculate Cellwise Flags for Anomaly Detection
Description
The function uses fuzzy logic to determine if a data entry is an outlier or 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, weighted = NULL, ...)cellwise(a, contamination = 0.08, epochs = 1000L, weighted = NULL, ...)
Arguments
a |
A long-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. |
weighted |
A string indicating the isolation forest algorithm ( |
... |
Additional arguments that are passed to the functions executing the isolation algorihtms (or not used if |
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
characterorfactorcolumn containing the information on the stratification. "unit_id"-
a
characterorfactorcolumn containing the ID of the statistical unit in the survey sample(x, size, replace = FALSE, prob = NULL). "master_varname"-
a
charactercolumn containing the name of the observed variable. "current_value_num"-
a
numericthe observed value, i.e., a data entrie "pred_value"-
a
numerica value observed on a previous survey for the same variable if available. If not available, the value can be set toNAorNaN. 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
A data frame with the same columns as the input data frame, plus the following additional columns:
- zScore
-
The z-score of the cell.
- hScore
-
The h-score of the cell.
- rScore
-
The r-score of the cell.
- tScore
-
The t-score of the cell.
- score
-
The final outlier score of the cell.
- outlier
-
A boolean indicating whether the cell is an outlier.
- anomaly_flag
-
A character string indicating the type of anomaly detected, if any.
Author(s)
Luca Sartore [email protected]
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)# 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)
Evaluate the Accuracy of Outlier Classification Results
Description
The function computes the confusion matrix between the logical output of an outlier detection algorithm and a reference (ground-truth) logical vector. The function also calculates the overall accuracy of the results from the confusion matrix, including recall, precision, and F1-scores for the two classes (regular, versus outlier).
Usage
class_check(pred, truth)class_check(pred, truth)
Arguments
pred |
A logical vector with the classification output from an anomaly detection algorithm. |
truth |
A logical vector with the observed classification as a reference (or ground truth). |
Details
The function computes the confusion matrix using the function table. True positive and false negative are successively evaluated to compute overall accuracy, recall, precision, and F1-scores.
Value
An S3 class named checkwise with the confusion matrix, and other accuracy metrices appended as attribues.
attr(, "overall") A numeric value between zero and one with the overall accuracy.
attr(, "recall") A numeric vector of values between zero and one with the recall index for regular and outlier cells.
attr(, "precision") A numeric vector of values between zero and one with the precision index for regular and outlier cells.
attr(, "f1-score") A numeric vector of values between zero and one with the F1-scores for regular and outlier cells.
Author(s)
Luca Sartore [email protected]
Examples
# Load the package library(HRTnomaly) set.seed(2025L) # Load the 'toy' data data(toy) # Detect cellwise outliers using Cellwise Analysis res <- cellwise(toy[sample.int(100), ], 0.05, 2L) class_check(res$outlier, res$anomaly_flag != "")# Load the package library(HRTnomaly) set.seed(2025L) # Load the 'toy' data data(toy) # Detect cellwise outliers using Cellwise Analysis res <- cellwise(toy[sample.int(100), ], 0.05, 2L) class_check(res$outlier, res$anomaly_flag != "")
Deep Isolation Forest
Description
The function builds a deep isolation forest that uses fuzzy logic to determine if a record is anomalous or not.
The function takes a wide-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 records are outliers (TRUE) or not (FALSE).
Usage
dif(dta, nt = 100L, nss = NULL, threshold = 0.95)dif(dta, nt = 100L, nss = NULL, threshold = 0.95)
Arguments
dta |
A wide-format |
nt |
Number of deep isolation trees to build to form the forest. By default, it is set to |
nss |
Number of subsamples used to build a single deep isolation tree.
If set (by default) to |
threshold |
A number between zero and one used as a threshold when identifying outliers from the anomaly scores.
By default, this argument is set to |
Details
The argument dta is provided as an object of class data.frame.
This object is considered as a wide-format data.frame.
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 wide-format data.frame is provided as input data and contains extra columns:
- scores
-
A numeric vector of anomaly scores ranging from 0 to 1, where values closer to 1 indicate higher anomaly.
- flags
-
A logical vector indicating whether each record is flagged as an outlier (
TRUE) or not (FALSE) based on the specifiedthreshold.
Author(s)
Luca Sartore [email protected]
Examples
# Load the package library(HRTnomaly) set.seed(2025L) # Detect outliers in the `iris` dataset res <- dif(iris)# Load the package library(HRTnomaly) set.seed(2025L) # Detect outliers in the `iris` dataset res <- dif(iris)
Calculate Cellwise Flags for Anomaly Detection
Description
The function uses fuzzy logic to determine if a data entry is an outlier or 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
fuzzyHRT(a, contamination = 0.08)fuzzyHRT(a, contamination = 0.08)
Arguments
a |
A long-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. |
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
characterorfactorcolumn containing the information on the stratification. "unit_id"-
a
characterorfactorcolumn containing the ID of the statistical unit in the survey sample(x, size, replace = FALSE, prob = NULL). "master_varname"-
a
charactercolumn containing the name of the observed variable. "current_value_num"-
a
numericthe observed value, i.e., a data entrie "pred_value"-
a
numerica value observed on a previous survey for the same variable if available. If not available, the value can be set toNAorNaN. 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 [email protected]
Examples
# Load the package library(HRTnomaly) set.seed(2025L) # Load the 'toy' data data(toy) # Detect cellwise outliers res <- fuzzyHRT(toy[sample.int(100), ])# Load the package library(HRTnomaly) set.seed(2025L) # Load the 'toy' data data(toy) # Detect cellwise outliers res <- fuzzyHRT(toy[sample.int(100), ])
Generalized Isolation Forest
Description
The function builds a generalized isolation forest that uses fuzzy logic to determine if a record is anomalous or not.
The function takes a wide-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 records are outliers (TRUE) or not (FALSE).
Usage
gif(dta, nt = 100L, nss = NULL, threshold = 0.95)gif(dta, nt = 100L, nss = NULL, threshold = 0.95)
Arguments
dta |
A wide-format |
nt |
Number of generalized isolation trees to build to form the forest. By default, it is set to |
nss |
Number of subsamples used to build a single generalized isolation tree.
If set (by default) to |
threshold |
A number between zero and one used as a threshold when identifying outliers from the anomaly scores.
By default, this argument is set to |
Details
The argument dta is provided as an object of class data.frame.
This object is considered as a wide-format data.frame.
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 wide-format data.frame is provided as input data and contains extra columns:
- scores
-
A numeric vector of anomaly scores ranging from 0 to 1, where values closer to 1 indicate higher anomaly.
- flags
-
A logical vector indicating whether each record is flagged as an outlier (
TRUE) or not (FALSE) based on the specifiedthreshold.
Author(s)
Luca Sartore [email protected]
Examples
# Load the package library(HRTnomaly) set.seed(2025L) # Detect outliers in the `iris` dataset res <- gif(iris)# Load the package library(HRTnomaly) set.seed(2025L) # Detect outliers in the `iris` dataset res <- gif(iris)
Proximity Isolation Forest
Description
The function builds a proximity isolation forest that uses fuzzy logic to determine if a record is anomalous or not.
The function takes a list object as input and returns it with two vectors appended as attributes.
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 records are outliers (TRUE) or not (FALSE).
Usage
pif(dta, nt = 100L, nss = NULL, max_depth = 12L, threshold = 0.95, proximity_type = c("single", "paired", "pivotal"), dist_fun = NULL)pif(dta, nt = 100L, nss = NULL, max_depth = 12L, threshold = 0.95, proximity_type = c("single", "paired", "pivotal"), dist_fun = NULL)
Arguments
dta |
A |
nt |
Number of deep isolation trees to build to form the forest. By default, it is set to |
nss |
Number of subsamples used to build a proximity isolation tree in the forest.
If set (by default) to |
max_depth |
An integer number corresponding to the maximum depth achieved by a proximity isolation tree in the forest.
By default, this argument is set to |
threshold |
A number between zero and one used as a threshold when identifying outliers from the anomaly scores.
By default, this argument is set to |
proximity_type |
A character string denoting the number the number of proximity prototypes used by the algorithms (see details for more information).
By default, a |
dist_fun |
A function computing the distance between any pair of components in |
Details
The argument dta is provided as an object of class list.
This object is considered as a list of arbitrary R objects that will be analyzed by one of the three algorithms provided with the pif function.
Three algorithms are implemented. The user can choose the proximity type by providing the number of prototypes used to build the isolation trees in the forest. A "single" prototype uses the distance between an input data point to a single randomly selected prototype at each branching node of the tree. Two prototypes (denoted as "paired") are randomly chosen and successively considered as gravitational point of their respective basins of attraction for partitioning the data. An additional "pivotal" point is randomly selected to enhance the algorithm based on two prototypes. In this case, the two distances between a data point and the two prototypes are normalized through the Steinhaus transformation and the pivotal prototype.
Value
The original input list dta with the following attributes appended:
- scores
-
A numeric vector of anomaly scores, ranging from 0 to 1, where higher values indicate a higher likelihood of being an outlier.
- flag
-
A logical vector indicating whether each element in the input list is flagged as an outlier (
TRUE) or not (FALSE) based on the specifiedthreshold.
Author(s)
Luca Sartore [email protected]
Examples
# Load the package library(HRTnomaly) set.seed(2025L) # Personalized distance my_dst <- function(x, y) { xn <- as.numeric(x[[1]][1:4]) yn <- as.numeric(y[[1]][1:4]) num <- mean((xn - yn)^2) den <- median((xn - yn)^2) return(num / (1 + den)) } # Converting the dataset iris to a list ir <- apply(iris, 1, list) # Detect outliers in the `iris` dataset res_sng <- pif(ir, 5L, 18L, 5L, .85, "single", my_dst) res_prd <- pif(ir, 5L, 18L, 5L, .85, "paired", my_dst) res_prx <- pif(ir, 5L, 18L, 5L, .85, "pivotal", my_dst) # count identified anomalies print(sum(attr(res_prd, "flag")))# Load the package library(HRTnomaly) set.seed(2025L) # Personalized distance my_dst <- function(x, y) { xn <- as.numeric(x[[1]][1:4]) yn <- as.numeric(y[[1]][1:4]) num <- mean((xn - yn)^2) den <- median((xn - yn)^2) return(num / (1 + den)) } # Converting the dataset iris to a list ir <- apply(iris, 1, list) # Detect outliers in the `iris` dataset res_sng <- pif(ir, 5L, 18L, 5L, .85, "single", my_dst) res_prd <- pif(ir, 5L, 18L, 5L, .85, "paired", my_dst) res_prx <- pif(ir, 5L, 18L, 5L, .85, "pivotal", my_dst) # count identified anomalies print(sum(attr(res_prd, "flag")))
A Method to Print the Accuracy of Outlier Classification Results
Description
The function prints the confusion matrix and accuracy results previously computed with the function class_check.
Usage
## S3 method for class 'checkwise' print(x, confusion = FALSE, ...)## S3 method for class 'checkwise' print(x, confusion = FALSE, ...)
Arguments
x |
An S3 object of the class |
confusion |
A logical value, which is |
... |
Additional arguments to pass to the function |
Details
The function computes the confusion matrix using the function table. True positive and false negative are successively evaluated to compute overall accuracy, recall, precision, and F1-scores.
Value
An S3 class named checkwise with the confusion matrix, and other accuracy metrics appended as attribues.
attr(, "overall") A numeric value between zero and one with the overall accuracy.
attr(, "recall") A numeric vector of values between zero and one with the recall index for regular and outlier cells.
attr(, "precision") A numeric vector of values between zero and one with the precision index for regular and outlier cells.
attr(, "f1-score") A numeric vector of values between zero and one with the F1-scores for regular and outlier cells.
Author(s)
Luca Sartore [email protected]
Examples
# Load the package library(HRTnomaly) set.seed(2025L) # Load the 'toy' data data(toy) # Detect cellwise outliers using Bayesian Analysis res <- cellwise(toy[sample.int(100), ], 0.5, 10L) print(class_check(res$outlier, res$anomaly_flag != ""))# Load the package library(HRTnomaly) set.seed(2025L) # Load the 'toy' data data(toy) # Detect cellwise outliers using Bayesian Analysis res <- cellwise(toy[sample.int(100), ], 0.5, 10L) print(class_check(res$outlier, res$anomaly_flag != ""))
Set the number of CPU cores for HPC
Description
The function set the number of CPU cores for parallel computation by the use of OpenMP library (https://www.openmp.org/). If the package was not complied with the library OpenMP (>= 3.0), this function is disabled.
Usage
setCores(n)setCores(n)
Arguments
n |
an integer value denoting the number of CPU cores to use; if it exceeds the total number of cores, all of them will be used. If missing, the number of CPU cores in use will be displayed. |
Details
When the package is loaded, only one CPU core is used.
Value
The total number of CPU cores in use will be returned and a message will be displayed. If the package was not complied with the library OpenMP (>= 3.0), the value one will be returned.
Author(s)
Luca Sartore [email protected]
References
SunTM ONE Studio 8 (2003) OpenMP API User's Guide. Sun Microsystems Inc., Santa Clara, U.S.A.
Examples
#Display the number of CPU cores in use setCores() #Set 2 CPU cores for parallel computation setCores(2) #Set 1 CPU core for serial computation setCores(1)#Display the number of CPU cores in use setCores() #Set 2 CPU cores for parallel computation setCores(2) #Set 1 CPU core for serial computation setCores(1)
Toy dataset
Description
Synthetically produced data are provided to test the FuzzyHRT algorithm.
Usage
data(toy)data(toy)
Format
The toy data is a data.frame object consisting of 9265 synthetic records and 5 variables:
strata-
a numeric value of the strata
unit_id-
a character string with the record ID
master_varname-
a character string with the names of the variables
current_value_num-
a numeric value with the current synthetic observation
pred_value-
a numeric value with the previously synthetic observation
Examples
# Load the package library(HRTnomaly) set.seed(2025L) # Load the toy dataset data(toy) # Detect cellwise outliers res <- fuzzyHRT(toy)# Load the package library(HRTnomaly) set.seed(2025L) # Load the toy dataset data(toy) # Detect cellwise outliers res <- fuzzyHRT(toy)