mnntsgofdesignmatrix {CircNNTSRmult}

Design Matrix of the MNNTS Goodness of Fit Test

Description

Computes the design matrix of the auxiliary regression for the goodness of fit test of an MNNTS density based on the estimated characteristic function

Usage

mnntsgofdesignmatrix(data,charfunarray,R=1)

Arguments

data

Matrix of angles in radians (with R columns)

charfunarray

A data frame (matrix) with the support and values of the characteristic function of the MNNTS density obtained by using the function mnntscharacteristic function with vector of parameters M of dimension R

R

Number of dimensions

Value

A matrix that is the design matrix to run the auxiliary regression for the goodness of fit test

Author(s)

Juan Jose Fernandez-Duran and Maria Mercedes Gregorio-Dominguez

References

Fernandez-Duran and J. J. and Gregorio-Dominguez and M. M (2023). Multivariate Nonnegative Trigonometric Sums Distributions for High-Dimensional Multivariate Circular Data, arXiv preprint arXiv:2301.03643v2

Fan, Y. (1997). Goodness-of-fit tests for a multivariate distribution by the empirical characteristic function. Journal of Multivariate Analysis, 62, 36-63.

Examples

# A characteristic function from a bivariate MNNTS density

set.seed(200)
Mbiv<-c(2,3)
Rbiv<-length(Mbiv)
data(Nest)
data<-Nest*(pi/180)
est<-mnntsmanifoldnewtonestimation(data,Mbiv,Rbiv,70)
est
charfunbiv23<-mnntscharacteristicfunction(cestimatesarray=est$cestimates,M=Mbiv,R=Rbiv)
charfunbiv23
designmatrix23<-mnntsgofdesignmatrix(data,charfunbiv23,R=2)
designmatrix23

# A characteristic function from a trivariate MNNTS density

set.seed(200)
Mtriv<-c(2,3,3)
Rtriv<-length(Mtriv)
data(WindDirectionsTrivariate)
data<-WindDirectionsTrivariate
est<-mnntsmanifoldnewtonestimation(data,Mtriv,Rtriv,40)
est
charfuntriv233<-mnntscharacteristicfunction(cestimatesarray=est$cestimates,M=Mtriv,R=Rtriv)
charfuntriv233
designmatrix233<-mnntsgofdesignmatrix(data,charfuntriv233,R=3)
designmatrix233