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

In iAR: Irregularly Observed Autoregressive Models

Test for the significance of the autocorrelation estimated by the iAR package models in periodic irregularly observed time series

Description

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

Usage

iARTest(
  series,
  times,
  series_esd = 0,
  f,
  coef,
  model = "iAR",
  plot = TRUE,
  xlim = c(-1, 0),
  df = 3
)

Arguments

series	Array with the time series observations.
times	Array with the irregular observational times.
series_esd	Array with the variance of the measurement errors.
f	Frequency (1/Period) of the raw time series.
coef	autocorrelation estimated by one of the iAR package models.
model	model used to estimate the autocorrelation parameter ("iAR", "iAR-Gamma", "iAR-T", "CiAR" or "BiAR").
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.
df	degrees of freedom parameter of the iAR-T model.

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:

phi	MLE of the autocorrelation parameter of the iAR/CiAR model.
bad	A matrix with two columns. The first column contains the incorrect frequencies used to fit each harmonic model. The second column has the MLEs of the autocorrelation parameters of the iAR/CiAR model that has been fitted to the residuals of the harmonic model fitted using the frequencies of the first column.
norm	Mean and variance of the normal distribution of the bad fitted examples.
z0	Statistic of the test (log(abs(phi))).
pvalue	P-value computed for the test.

References

\insertRef

Eyheramendy_2018iAR

See Also

clcep, harmonicfit,iARPermutation

Examples

data(clcep)
f1=0.060033386
#results=harmonicfit(file=clcep,f1=f1)
#y=results$res/sqrt(var(results$res))
#st=results$t
#res3=iARloglik(y,st,standardized=TRUE)[1]
#res3$coef
#require(ggplot2)
#test<-iARTest(series=clcep[,2],times=clcep[,1],f=f1,coef=res3$coef,
#model="iAR",plot=TRUE,xlim=c(-10,0.5))
#test

iAR documentation built on April 4, 2025, 2:21 a.m.