| MaxSkew {MaxSkew} | R Documentation |
MaxSkew: skewness-based projection pursuit
Description
Finds Orthogonal Data Projections with Maximal Skewness
Usage
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