mnntsgofstatistics: Statistics of the MNNTS Goodness of Fit Test

In CircNNTSRmult: Multivariate Circular Data using MNNTS Models

Statistics of the MNNTS Goodness of Fit Test

Description

Computes the statistics of the goodness of fit test of an MNNTS density based on the estimated characteristic function

Usage

mnntsgofstatistics(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 mnntscharacteristicfunction with vector of parameters M of dimension R
R	Number of dimensions

Value

gofstat	The value of the goodness of fit statistic
gofstatnormal	The value of the normal approximation o fthe goodnes of fit statistic

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
gofstats23<-mnntsgofstatistics(data,charfunbiv23,R=2)
gofstats23

# 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,50)
est
charfuntriv233<-mnntscharacteristicfunction(cestimatesarray=est$cestimates,M=Mtriv,R=Rtriv)
charfuntriv233
gofstats233<-mnntsgofstatistics(data,charfuntriv233,R=3)
gofstats233

CircNNTSRmult documentation built on Sept. 12, 2023, 9:06 a.m.