mvOutlier: Multivariate Outlier Detection

Description

This function detects multivariate outliers based on Mahalanobis distance and adjusted Mahalanobis distance.

Usage

mvOutlier(data, qqplot = TRUE, alpha = 0.5, tol = 1e-25, method = c("quan", "adj.quan"),  label = TRUE, position = NULL, offset = 0.5)

Arguments

data

a numeric matrix or data frame

qqplot

if TRUE it creates a chi-square Q-Q plot

alpha

a numeric parameter controlling the size of the subsets over which the determinant is minimized. Allowed values for the alpha are between 0.5 and 1 and the default is 0.5.

tol

a numeric tolerance value which isused for inversion of the covariance matrix (default = 1e-25).

method

quan for Mahalanobis distance and adj.quan for adjusted Mahalanobis distance.

label

an optional term to display outlier labels (i.e. observation number) on the Q-Q plot.

position

a position specifier for the text. Values of 1, 2, 3 and 4, respectively indicate positions below, to the left of, above and to the right of the specified coordinates.

offset

when pos is specified, this value gives the offset of the label from the specified coordinate in fractions of a character width.

Value

outlier: an outlier set
newdData: new data set without possible outliers

Examples

Run this code

setosa = iris[1:50, 1:3] # Iris data only for setosa and three variables
result = mvOutlier(setosa, qqplot = TRUE, method = "quan", label = TRUE)
result

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