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

In MultiLCIRT: Multidimensional Latent Class Item Response Theory Models

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)

Example output

Loading required package: MASS
Loading required package: limSolve

MultiLCIRT documentation built on May 2, 2019, 2:38 p.m.