HTP2: Production Measurements of High-Tech Parts - Singular Case

In ICSOutlier: Outlier Detection Using Invariant Coordinate Selection

Production Measurements of High-Tech Parts - Singular Case

Description

The HTP2 data set contains 457 high-tech parts designed for consumer products characterized by 149 tests. These tests are performed to ensure a high quality of the production. All these 457 parts were considered functional and have been sold. However the part 28 showed defects in use and was returned to the manufacturer by the customer. Therefore this part can be considered as outlier.

Usage

data("HTP2")

Format

A data frame with 457 rows and 149 variables V.1 - V.149, presenting some collinearity issues.

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

# HTP2 data: the observation 28 is considered as an outlier
data("HTP2")
outliers <- c(28)
boxplot(HTP2, horizontal = TRUE)
# Outlier detection using ICS
library(ICS)
## Not run: 
out <- ICS_outlier(HTP2, ICS_algorithm = "QR",
                   method = "norm_test",
                   test = "agostino.test", level_test = 0.05,
                   level_dist = 0.01, n_dist = 50)

# Here there is a singularity issue. One solution is to first reduce the 
# dimension. To ensure higher numerical stability of the subsequent methods
# we suggest to permute the data and to use the QR decomposition instead of
# the regular SVD decomposition.
Xt <- HTP2
# Normalization by the mean 
Xt.c <- sweep(HTP2, 2, colMeans(HTP2), "-")

# Permutation by rows 
# decreasing by infinity norm:  absolute maximum
norm_inf <- apply(Xt.c, 1, function(x) max(abs(x)))
order_rows <- order(norm_inf, decreasing = TRUE)
Xt_row_per <- Xt.c[order_rows,]

# QR decomposition of Xt with column pivoting from LAPACK 
qr_Xt <- qr(1/sqrt(nrow(Xt.c)-1)*Xt_row_per, LAPACK = TRUE)

# Estimation of rank q 
# R is nxp, but with only zero for rows > p
# the diag of R is already in decreasing order and is a good approximation
# of the rank of X.c. To decide on which singular values are zero we use
# a relative criteria based on previous values.
# R should be pxp
R <- qr.R(qr_Xt)
r_all <- abs(diag(R))
r_ratios <- r_all[2:length(r_all)]/r_all[1:(length(r_all)-1)]
q <- which(r_ratios < max(dim(Xt.c)) *.Machine$double.eps)[1]
q <- ifelse(is.na(q), length(r_all), q)

# Q should be nxp but we are only interested in nxq
Q1 <- qr.Q(qr_Xt)[,1:q]

# QR decomposition of Rt 
R_q <- R[1:q, ]
qr_R <- qr(t(R_q), LAPACK = TRUE)
Tau <- qr.Q(qr_R)[1:q, ]
Omega1 <- qr.R(qr_R)[1:q, 1:q]

# New X tilde 
# permutation matrices
# permutation of rows
Pi2 <- data.frame(model.matrix(~ . -1, data = data.frame(row=as.character(order_rows))))
Pi2 <- Pi2[,order(as.numeric(substr(colnames(Pi2), start = 4, stop = nchar(colnames(Pi2)))))]
colnames(Pi2) <- rownames(Xt)

# permutation of cols
Pi3 <- data.frame(model.matrix(~ . -1, data = data.frame(col=as.character( qr_R$pivot))))
Pi3 <- t(Pi3[,order(as.numeric(substr(colnames(Pi3), start = 4, stop = nchar(colnames(Pi3)))))])

X_tilde <- sqrt(nrow(Xt)-1)* Tau %*% t(Pi3) %*% t(Q1)

Xt_tilde <- t(Pi2) %*% t(X_tilde)

# Run ICS_outlier
out <- ICS_outlier(Xt_tilde, ICS_algorithm = "QR",
method = "norm_test",
test = "agostino.test", level_test = 0.01,
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)

## End(Not run)

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