HTP3: Production Measurements of High-Tech Parts - Nearly Singular...

In ICSOutlier: Outlier Detection Using Invariant Coordinate Selection

Production Measurements of High-Tech Parts - Nearly Singular Case

Description

The HTP3 data set contains 371 high-tech parts designed for consumer products characterized by 33 tests. These tests are performed to ensure a high quality of the production. All these 371 parts were considered functional and have been sold. However the part 32 showed defects in use and was returned to the manufacturer by the customer. Therefore this part can be considered as outlier.

Usage

data("HTP3")

Format

A data frame with 371 rows and 33 variables V.1 - V.33, presenting some approximate collinearity issues which may cause some numerical inaccuracies.

Source

Anonymized data from a nondisclosed manufacturer.

References

Archimbaud, A., Drmac, Z., Nordhausen, K., Radojcic, U. and Ruiz-Gazen, A. (2023) Numerical Considerations and a New Implementation for Invariant Coordinate Selection. SIAM Journal on Mathematics of Data Science, 5(1), 97–121. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1137/22M1498759")}.

Examples

# HTP3 data: the observation 32 is considered as an outlier
data("HTP3")
outliers <- c(32)
boxplot(HTP3)

# Outlier detection using ICS
library(ICS)
out <- ICS_outlier(HTP3, ICS_algorithm = "QR",
                   method = "norm_test",
                   test = "agostino.test", level_test = 0.05,
                   level_dist = 0.01, n_dist = 50)

summary(out)
plot(out)
text(outliers, out$ics_distances[outliers], outliers, pos = 2, cex = 0.9, col = 2)

ICSOutlier documentation built on May 29, 2024, 2:08 a.m.