snntsmanifoldnewtonestimation | R Documentation |
Computes the maximum likelihood estimates of the SNNTS model parameters using a Newton algorithm on the hypersphere
snntsmanifoldnewtonestimation(data, M = c(0,0), iter = 1000,
initialpoint = FALSE, cinitial)
data |
Matrix of angles in radians, with one row for each data point. The first column contains longitude data (between zero and 2*pi), and second column contains latitude data (between zero and pi), with one row for each data point |
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, avector of complex numbers of dimension prod(M+1). The first element is a real and positive number. The sum of the squared moduli of the c parameters must be equal to one. |
cestimates |
Matrix of prod(M+1)*(3). The first two columns are the parameter numbers, and the last column is the c parameter's estimators |
loglik |
Optimum log-likelihood value |
AIC |
Value of Akaike's Information Criterion |
BIC |
Value of Bayesian Information Criterion |
gradnormerror |
Gradient error after the last iteration |
The parameters cinitial and cestimates used by this function are the transformed parameters of the SNNTS density function, which lie on the surface of the unit hypersphere
Juan Jose Fernandez-Duran and Maria Mercedes Gregorio-Dominguez
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
set.seed(200)
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),100,TRUE,cpar)
cest
cest<-snntsmanifoldnewtonestimation(data,c(1,2),100)
cest
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