IAR.Test: Test for the significance of the autocorrelation estimated by...

Description Usage Arguments Details Value References See Also Examples

View source: R/IARtest.R

Description

This function perform a test for the significance of the autocorrelation estimated by the IAR model. This test is based on the residuals of the periodical time series fitted with an harmonic model using an incorrect period.

Usage

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IAR.Test(y, sT, f, phi, plot = "TRUE", xlim = c(-1, 0))

Arguments

y

Array with the time series observations

sT

Array with the irregular observational times

f

Frequency (1/Period) of the raw time series

phi

autocorrelation estimated by IAR.loglik

plot

logical; if true, the function return a density plot of the distribution of the bad fitted examples; if false, this function does not return a plot

xlim

The x-axis limits (x1, x2) of the plot. Only works if plot='TRUE'. See plot.default for more details

Details

The null hypothesis of the test is: The autocorrelation estimated in the time series belongs to the distribution of the coefficients estimated for the residuals of the data fitted using wrong periods. Therefore, if the hypothesis is rejected, it can be concluded that the residuals of the harmonic model do not remain a time dependency structure.The statistic of the test is log(phi) which was contrasted with a normal distribution with parameters corresponding to the log of the mean and the variance of the phi computed for the residuals of the bad fitted light curves.

Value

A list with the following components:

References

\insertRef

Eyheramendy_2018iAR

See Also

clcep, harmonicfit, IAR.loglik, IAR.Test2

Examples

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data(clcep)
f1=0.060033386
results=harmonicfit(file=clcep,f1=f1)
y=results$res/sqrt(var(results$res))
sT=results$t
res3=IAR.loglik(y,sT,standarized='TRUE')[1]
res3$phi
require(ggplot2)
test<-IAR.Test(y=clcep[,2],sT=clcep[,1],f1,res3$phi,plot='TRUE',xlim=c(-10,0.5))
test

iAR documentation built on April 15, 2021, 5:06 p.m.

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