SOPIE (version 1.4)

a.estimate: Estimate the Left Endpoint of the Off-Pulse Interval of a Pulsar

Description

a.estimate and b.estimate is almost identical function. a.estimate is the function used to obtain the estimated values of $a$, i.e. $\hat a$, for the off-pulse interval of a pulsar light curve. b.estimate is the function used to obtain the estimated values of $b$, i.e. $\hat b$, for the off-pulse interval of a pulsar light curve.

Usage

a.estimate(data, to = 1, min_points, alpha = 0.05, g = 1, r = 1)

Arguments

data

the data vector used to estimate $a$.

to

the value of the maximum domain of the data. Values will usually either be 1 or 2$\pi$.

min_points

the scalar or vector containing the value(s) of the minimum point(s) calculated during the kernel density estimation. This argument does not represent the index value(s) of the observations within data. The minimum point(s) can be obtained with the func

alpha

significance level ($\alpha$) that will be used during the sequential application of the goodness-of-fit tests for uniformity when estimating the off-pulse interval.

g

the value of the incremental growth of each subsequent interval over which uniformity is tested. In the suggested procedure, uniformity is sequentially tested, with the interval used in the test growing by g observations after every iterat

r

the number of subsequent intervals that must result in the rejection of uniformity before the function will stop. The choice of r must therefore be linked to the choice of g as explained above. For smaller values of g

Value

• a list containing the following components:
• summary
  a vector containing the estimated value of $a$, i.e. $\hat a$, for each of the four goodness-of-fit tests, namely the Anderson-Darling, Kolmogorov-Smirnov, Cramer-von Mises and the Rayleigh goodness-of-fit test.
• general
  a list containing the function call, the minimum value(s) used in the estimation, the level of significance ($\alpha$), the value of g and the value of r.

Examples

## This function is to be used inside the wrapper function SOPIE

simdata<-von_mises_sim(n=5000,k=1,c=0.3,noise=0.2)
SOPIE(simdata,h=1,to=1,alpha=0.05,g=5,r=10,m=1,grid=100)