mvTopCoding: Detection and winsorization of multivariate outliers

In sdcMicro: Statistical Disclosure Control Methods for Anonymization of Data and Risk Estimation

Detection and winsorization of multivariate outliers

Description

Imputation and detection of outliers

Usage

mvTopCoding(x, maha = NULL, center = NULL, cov = NULL, alpha = 0.025)

Arguments

x	an object coercible to a data.table containing numeric entries
maha	squared mahalanobis distance of each observation
center	center of data, needed for calculation of mahalanobis distance (if not provided)
cov	covariance matrix of data, needed for calcualtion of mahalanobis distance (if not provided)
alpha	significance level, determining the ellipsoide to which outliers should be placed upon

Details

Winsorizes the potential outliers on the ellipsoid defined by (robust) Mahalanobis distances in direction to the center of the data

Value

the imputed winsorized data

Author(s)

Johannes Gussenbauer, Matthias Templ

Examples

set.seed(123)
x <- MASS::mvrnorm(20, mu = c(5,5), Sigma = matrix(c(1,0.9,0.9,1), ncol = 2))
x[1, 1] <- 3
x[1, 2] <- 6
plot(x)
ximp <- mvTopCoding(x)
points(ximp, col = "blue", pch = 4)

# more dimensions
Sigma <- diag(5)
Sigma[upper.tri(Sigma)] <- 0.9
Sigma[lower.tri(Sigma)] <- 0.9
x <- MASS::mvrnorm(20, mu = rep(5,5), Sigma = Sigma)
x[1, 1] <- 3
x[1, 2] <- 6
pairs(x)

ximp <- mvTopCoding(x)
xnew <- data.frame(rbind(x, ximp))
xnew$beforeafter <- rep(c(0,1), each = nrow(x))
pairs(xnew, col = xnew$beforeafter, pch = 4)

# by hand (non-robust)
x[2,2] <- NA
m <- colMeans(x, na.rm = TRUE)
s <- cov(x, use = "complete.obs")
md <- stats::mahalanobis(x, m, s)
ximp <- mvTopCoding(x, center = m, cov = s, maha = md)
plot(x)
points(ximp, col = "blue", pch = 4)

sdcMicro documentation built on Sept. 27, 2023, 5:07 p.m.