Generate AR(1) surrogates. Implement shuffling algorithm of Meyers (2012) if desired.
ar1(npts=1024,dt=1,mean=0,sdev=1,rho=0.9,shuffle=F,nsim=1,genplot=T,verbose=T)
number of time series data points
sampling interval
mean value for AR1 surrogate series
standard deviation for AR1 surrogate series
AR(1) coefficient
Apply secondary shuffle of Gaussian deviates before AR modeling
Number of AR1 surrogate series to generate
generate summary plots (T or F)
verbose output (T or F)
These simulations use the random number generator of Matsumoto and Nishimura [1998]. If shuffle = T, the algorithm from Meyers (2012, pg. 11) is applied: (1) two sets of random sequences of the same length are generated, (2) the first random sequence is then sorted, and finally (3) the permutation vector of the sorted sequence is used to reorder the second random number sequence. This is done to guard against potential shortcomings in random number generation that are specific to spectral estimation.
S.R. Meyers, 2012, Seeing red in cyclic stratigraphy: Spectral noise estimation for astrochronology: Paleoceanography, v. 27, PA3328.