nntsmanifoldnewtonestimationinterval0to1 {CircNNTSR}

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)