MultiLCIRT (version 2.11)

standard.matrix: Standardization of a matrix of support points on the basis of a vector of probabilities

Description

Given a matrix of support points X and a corresponding vector of probabilities piv it computes the mean for each dimension, the variance covariance matrix, the correlation matrix, Spearman correlation matrix, and the standarized matrix Y

Usage

standard.matrix(X,piv)

Arguments

X

matrix of support points for the distribution included row by row

piv

vector of probabilities with the same number of elements as the rows of X

Value

mu

vector of the means

V

variance-covariance matrix

si2

vector of the variances

si

vector of standard deviations

Cor

Braives-Pearson correlation matrix

Sper

Spearman correlation matrix

Y

matrix of standardized support points

Examples

## Example of standardization of a randomly generated distribution
X = matrix(rnorm(100),20,5)
piv = runif(20); piv = piv/sum(piv)
out = standard.matrix(X,piv)