View source: R/nntsmanifoldnewtonestimationinterval0to2pi.R
nntsmanifoldnewtonestimationinterval0to2pi | R Documentation |
Parameter estimation for incidence data (number of observed values in certain intervals defined over [0,2*pi))
nntsmanifoldnewtonestimationinterval0to2pi(data, cutpoints,
subintervals,M = 0, iter=1000, initialpoint = FALSE, cinitial)
data |
Frequency of data on each interval |
cutpoints |
Vector with the limits of intervals. The length of cutpoints has to 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 |
A 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) |
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 log-likelihood value |
AIC |
Value of Akaike's Information Criterion |
BIC |
Value of Bayesian Information Criterion |
gradnormerror |
Gradient error after last iteration |
Juan Jose Fernandez-Duran y Maria Mercedes Gregorio-Dominguez
data<-c(1,2,6,4)
cutpoints<-c(0,pi/2,pi,3*pi/2,2*pi-0.00000001)
nntsmanifoldnewtonestimationinterval0to2pi(data, cutpoints, length(data),1)
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