outlier: Outliers Detection

In MVET: Multivariate Estimates and Tests

Outliers Detection

Description

Using the mardia test, outliers are detected based on skewness and kurtosis. However, outliers don't detect more than half of the total observation data.(Can be modified with the lim option.)

Usage

outlier(data,
        lim = 0,
        level = 0.05,
        option = "all")

Arguments

data	A numeric matrix or data frame.
lim	The number of outliers detected can be limited. If 0 is entered, detection is possible up to half of the data. (default = 0)
level	The significance level of the skewness and kurtosis statistics of the "madiatest" function. (default = 0.05)
option	"skew" refers to skewness, "kurt" refers to kurtosis, "all" refers to skewness and kurtosis. Outliers are detected until the corresponding option in the mardiatest is “Accept”. (default = "all")

Value

modified.data	The modified data without outliers.
modified.mvn	The modified Mardia test result without outliers.
outlier.num	The position of outliers.
outlier.cnt	Total number of outliers.

References

Jobson, J. D.(1992). Applied Multivariate Data Analysis, Springer-Verlag, New York.

See Also

mardiatest

Examples

data(wine)
class2.wine <- subset(wine, class == 2)[, -1]
outlier(class2.wine, lim = 0, level = 0.05, option = "all")

MVET documentation built on June 22, 2024, 11:25 a.m.