MultiSkew-package: MultiSkew

In MultiSkew: Measures, Tests and Removes Multivariate Skewness

Description

Computes the third multivariate cumulant of either the raw, centered or standardized data. Computes the main measures of multivariate skewness, together with their bootstrap distributions. Finally, computes the least skewed linear projections of the data

Details

Package: MultiSkew

Type: Package

Title: Measures, Tests and Removes Multivariate Skewness

Version: 1.1.1

Date: 2017-06-13

Author: Cinzia Franceschini, Nicola Loperfido

Maintainer: Cinzia Franceschini <[e-mail]>

License: GPL-2

References

Bartoletti, S. and Loperfido, N. (2010). Modelling Air Pollution Data by the Skew-Normal Distribution. Stochastic Environmental Research & Risk Assessment 24, 513-517.

Loperfido, N. (2013). Skewness and the Linear Discriminant Function. Statistics & Probability Letters 83, 93-99.

Loperfido, N. (2014). Linear Transformations to Symmetry. Journal of Multivariate Analysis 129, 186-192.

Malkovich, J.F. and Afifi, A.A. (1973). On Tests for Multivariate Normality. J. Amer. Statist. Ass. 68, 176-179.

Mardia, K.V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika 57, 519-530.

Mori T.F., Rohatgi V.K. and Szekely G.J. (1993). On multivariate skewness and kurtosis. Theory Probab. Appl. 38, 547-551.

Examples

data(PM10_2006)
PM10_2006_matrix<-data.matrix(PM10_2006)

MinSkew(PM10_2006_matrix[,2:5],4)
PartialSkew(PM10_2006_matrix[,2:5])
SkewMardia(PM10_2006_matrix[,2:5])
Third(PM10_2006_matrix[,2:5], "raw")

#library(MaxSkew)

SkewBoot(PM10_2006_matrix[,2:5], 50, 50, "Directional")
SkewBoot(PM10_2006_matrix[,2:5], 50, 50, "Mardia")
SkewBoot(PM10_2006_matrix[,2:5], 50, 50, "Partial")

MultiSkew documentation built on May 2, 2019, 9:36 a.m.