MVNtestchar (version 1.1.3)

testunknown: Process the Samples Whose Distribution is to be Tested

Description

Create positive definite matrices without nuisance parameters. Tabulate distribution. Calculate goodness of fit

Usage

testunknown(x, pvector, k, diagnose.s = FALSE, diagnose = FALSE, 
    verbose = TRUE)

Arguments

x

Name of matrix or array.

pvector

Dimensionality of random vectors

k

Number of cuts per unit for diagonal elements of matrix. Program uses 2k cuts per unit for off-diagonal elements

diagnose.s

Logical T causes printing of diagnostic terms in internal called function(s)

diagnose

Logical. T causes printing of diagnostic content

verbose

Logical. T causes printing of function ID before and after running

Value

a list including elements

Distribution

List. Count of pd matrices within individual subcubes of pd space, 1 for each layer of list

Goodness of fit

List. Chi square test of goodness of fit to uniform distribution, 1 for each layer of list

Call

Call to testunknown function

References

Csorgo, M and Seshadri, V (1970). On the problem of replacing composite hypotheses by equivalent simple ones, Rev. Int. Statist. Instit., 38, 351-368 Csorgo,M and Seshadri,V (1971). Characterizing the Gaussian and exponential laws by mappings onto the unit interval, Z. Wahrscheinlickhkeitstheorie verw. Geb., 18, 333-339. Fairweather, WR (1973). A test for multivariate normality based on a characterization. Dissertation submitted in partial fulfillment of the requirements for the Doctor of Philosophy, University of Washington, Seattle WA.

Examples

# NOT RUN {
data(unknown.Np2)
testunknown(x=unknown.Np2, pvector=2, k=20, 
     diagnose.s = FALSE, diagnose = FALSE, verbose = TRUE)
# }