| mnntsgofstatistics {CircNNTSRmult} | R Documentation |
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