testunknown: Process the Samples Whose Distribution is to be Tested

In MVNtestchar: Test for Multivariate Normal Distribution Based on a Characterization

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

Author(s)

William R. Fairweather

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

data(unknown.Np2)
testunknown(x=unknown.Np2, pvector=2, k=20, 
     diagnose.s = FALSE, diagnose = FALSE, verbose = TRUE)

MVNtestchar documentation built on July 26, 2020, 1:07 a.m.