It finds pvalue for tesing periodicity for any series using RSR.
pvalrsr(x, t=1:length(x), nf=150, Numpq =11)
Arguments
x: series to be tested for periodicity
t: vector of corresponding time points
nf: number of frequencies to enumerate
Numpq: Numebr of indices for interpolation in RSR
Details
A full RSR is integral part of the package. This was done using likelihood ratio statistic for simulated series from white noise process. For more information about the procedure, please see the first reference.
Returns
pvalue
References
Islam, M.S. (2008). Peridocity, Change Detection and Prediction in Microarrays. Ph.D. Thesis, The University of Western Ontario.
MacKinnon, J. G. (2001). Computing numerical distribution functions in econometrics. In proceedings of High Performance Computing Systems and Applications, edited by Pollard, A., Mewhort, D. J. and Weaver, D. F. Springer US. Vol. 451, 455-471.
Author(s)
M. S. Islam
Examples
# Non-Fourier frequencyz<-SimulateHReg(20, f=2.5/20,1,2)pvalrsr(z)# finding p-value using RSR# For comparing with Fisher's g test# library(GeneCycle)# fisher.g.test(z) # Fisher's g test# Fourier frequencyy<-SimulateHReg(20, f=2/20,1,2)pvalrsr(y)# finding p-value using RSR# For comparing with Fisher's g test# library(GeneCycle)# fisher.g.test(z) # Fisher's g test