R: Standardization of a matrix of support points on the basis of...
standard.matrix
R Documentation
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
Author(s)
Francesco Bartolucci, Silvia Bacci, Michela Gnaldi - University of Perugia (IT)
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)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
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R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(MultiLCIRT)
Loading required package: MASS
Loading required package: limSolve
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MultiLCIRT/standard.matrix.Rd_%03d_medium.png", width=480, height=480)
> ### Name: standard.matrix
> ### Title: Standardization of a matrix of support points on the basis of a
> ### vector of probabilities
> ### Aliases: standard.matrix
> ### Keywords: multivariate statistics
>
> ### ** 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)
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.