sdcMicro (version 5.5.1)

mvTopCoding: 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

object of class matrix with numeric entries

maha

squared mahalanobis distance of each observation

center

center of data, needed for calcualtion of mahalanobis distance (if not provide)

cov

covariance matrix of data, needed for calcualtion of mahalanobis distance (if not provide)

alpha

significance level, determining the ellipsoide to which outliers should be placed upon

Value

the imputed winsorized data

Details

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

Examples

# NOT RUN {
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
par(mfrow = c(1,2))
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)

# }