MaxSkew: MaxSkew: skewness-based projection pursuit

Description Usage Arguments Value Author(s) References

Description

Finds Orthogonal Data Projections with Maximal Skewness

Usage

1
MaxSkew(data, iterations, components, plot)

Arguments

data

Data matrix where rows and columns represent units and variables.

iterations

It is a positive integer

components

Number of orthogonal projections maximizing skewness. It is a positive integer smaller than the number of variables.

plot

Dichotomous variable: TRUE/FALSE. If plot is set equal to TRUE (FALSE) the scatterplot appears (does not appear) in the output.

Value

projectionmatrix

Matrix of projected data. The i-th row represents the i-th unit, while the j-th column represents the j-th projection.

pairs(projectionmatrix[,2:i],labels=values,main="Projections")

It is the multiple scatterplot of the projections maximizing skewness.

.projectionBIV

Vector of projected data when the original data are bivariate.The user can obtain a scatterplot of the projection by writing plot(.projectionBIV)

Author(s)

Cinzia Franceschini and Nicola Loperfido

References

de Lathauwer L., de Moor B.and Vandewalle J. (2000). Onthebestrank-1andrank-(R_1,R_2,...R_N) approximation of high-order tensors. SIAM Jour. Matrix Ana. Appl. 21, 1324-1342.

Loperfido, N. (2010). Canonical Transformations of Skew-Normal Variates. Test 19, 146-165.

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

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


MaxSkew documentation built on May 2, 2019, 8:26 a.m.