nntsmanifoldnewtonestimationinterval0to1: Parameter estimation for grouped data defined in [0,1)

In CircNNTSR: Statistical Analysis of Circular Data using Nonnegative Trigonometric Sums (NNTS) Models

Parameter estimation for grouped data defined in [0,1)

Description

Parameter estimation for incidence data (number of observed values in certain intervals defined over [0,1))

Usage

nntsmanifoldnewtonestimationinterval0to1(data, cutpoints, subintervals, M = 0, iter=1000, 
initialpoint = FALSE, cinitial)

Arguments

data	Frequency of data on each interval
cutpoints	Vector with the limits of intervals. The length of cutpoints must be one plus the length of the data
subintervals	Number of intervals
M	Number of components in the NNTS
iter	Number of iterations
initialpoint	TRUE if an initial point for the optimization algorithm will be used
cinitial	Vector of size M+1. The first element is real and the next M elements are complex (values for $c_0$ and $c_1, ...,c_M$).The sum of the squared moduli of the parameters must be equal to 1/(2*pi)

Value

cestimates	Matrix of M+1 * 2. The first column is the parameter numbers and the second column is the c parameter's estimators
loglik	Optimum loglikelihood value
AIC	Value of Akaike's Information Criterion
BIC	Value of Bayesian Information Criterion
gradnormerror	Gradient error after the last iteration

Author(s)

Juan Jose Fernandez-Duran y Maria Mercedes Gregorio-Dominguez

Examples

data<-c(1,2,4,6,1)
cutpoints<-c(0,0.2,0.4,0.6,0.8,0.999999999)
nntsmanifoldnewtonestimationinterval0to1(data, cutpoints, length(data), 1)

CircNNTSR documentation built on Sept. 1, 2023, 9:07 a.m.