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CircNNTSR (version 2.0)

snntsmanifoldnewtonestimation: Parameter estimation for SNNTS distributions for spherical data

Description

Computes the maximum likelihood estimates of the SNNTS model parameters, through a Newton algorithm on the hypersphere

Usage

snntsmanifoldnewtonestimation(data, M = c(0,0), iter = 1000,
initialpoint = FALSE, cinitial)

Arguments

data
a matrix of angles in radians, a row for each data point. Angles in first column (longitude) must be in the interval (0,2*pi), angles in second column (latitude) must be in the interval (0,pi)
M
vector with number of components in the SNNTS for each dimension
iter
Number of iterations
initialpoint
TRUE if an initial point for the optimization algorithm will be used
cinitial
initial value for cpars for the optimization algorithm. vector of complex numbers of dimension prod(M+1). The first element is a real and positive number. First M[1]+1 elements correspond to longitude, next M[2]+1 elements correspond to latitude. The

Value

  • cestimates
  • loglikOptimum loglikelihood value
  • AICValue of Akaike's Information Criterion
  • BICValue of Bayesian Information Criterion
  • gradnormerrorGradient's error after last iteration

References

Fernandez-Duran J. J. y Gregorio Dominguez, M. M. (2008) Spherical Distributions Based on Nonnegative Trigonometric Sums, Working Paper, Statistics Department, ITAM, DE-C08.6

Examples

Run this code
data(Datab6fisher_ready)
data<-Datab6fisher_ready

M<-c(4,4)
cpar<-rnorm(prod(M+1))+rnorm(prod(M+1))*complex(real=0,imaginary=1)
cpar[1]<-Re(cpar[1])
cpar<- cpar/sqrt(sum(Mod(cpar)^2))

cest<-snntsmanifoldnewtonestimation(data,c(4,4),5000,TRUE,cpar) 
cest
cest<-snntsmanifoldnewtonestimation(data,c(1,2),5000)
cest

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