View source: R/power.spectra.ens.R
computeSpectraEns | R Documentation |
Calculate ensemble power spectra using 4 spectral methods:
Lomb-Scargle periodogram
REDFIT (Mudelsee et al, 2009)
Multitaper Method (Thomson et al. 1982)
nuspectral (Mathias et al 2004) (no confidence estimation)
For REDFIT and MTM, only the 95 For multiple ensemble members, the median of the ensemble of 95 For Lomb-Scargle, the parameter 'probs' determines the quantiles of the surrogate spectrum distribution extracted as confidence limits.
computeSpectraEns(
time,
values,
max.ens = NA,
method = "mtm",
probs = 0.95,
gaussianize = TRUE,
ofac = 4,
padfac = 2,
tbw = 3,
wgtrad = 1,
sigma = 0.02,
mtm_null = "power_law"
)
time |
LiPD "variable list" or vector of year/age values |
values |
LiPD "variable list" or vector of values |
max.ens |
Maximum number of ensemble members to analyze |
method |
Method used to compute the spectra. Choose from:
|
probs |
vector of probabilities for the siginificance levels. To avoid such computations, pass 'probs = NA'. |
gaussianize |
Boolean flag indicating whether the values should be mapped to a standard Gaussian prior to analysis. |
ofac |
oversampling factor for lomb::lsp and dplR::redfit |
padfac |
padding factor for astrochron::mtm___ functions |
tbw |
time-bandwidth product for astrochron::mtm___ functions |
wgtrad |
radius of the nuspectral nuwaveletcoeff weight function (non-dimensional units) |
sigma |
decay parameter of the nuspectral nuwaveletcoeff wavelets |
mtm_null |
Method to estimate null hypothesis if method = "mtm". Valid choices are 'AR(1)', 'power_law' (default), or 'ML96' |
Calculate ensemble power spectra
a list of ensemble spectra results
freqs: vector of frequencies
power: vector of spectral powers
power.CL: matrix of confidence limits for spectral power
View a full-fledged example of how to use this function.
Ghil, M., Allen, R. M., Dettinger, M. D., Ide, K., Kondrashov, D., Mann, M. E., Robertson, A., Saunders, A., Tian, Y., Varadi, F., and Yiou, P.: Advanced spectral methods for climatic time series, Rev. Geophys., 40, 1003–1052, 2002.
Foster, G.: Wavelets for period analysis of unevenly sampled time series, Astron. Jour., 112, 1709, https://doi.org/10.1086/118137, 1996.
Mudelsee, M., D. Scholz, R. Röthlisberger, D. Fleitmann, A. Mangini, and E. W. Wolff (2009), Climate spectrum estimation in the presence of timescale errors, Nonlinear Processes in Geophysics, 16(1), 43–56, doi:10.5194/npg-16-43-2009.
Mathias, A., F. Grond, R. Guardans, D. Seese, M. Canela, and H. Diebner (2004), Algorithms for spectral analysis of irregularly sampled time series, Journal of Statistical Software, Articles, 11(2), 1–27, doi:10.18637/jss.v011.i02.
Thomson, D. J. (1982), Spectrum estimation and harmonic analysis, Proc. IEEE, 70(9), 1055–1096.
VanderPlas, J. T.: Understanding the Lomb–Scargle Periodogram, The Astrophysical Journal Supplement Series, 236, 16, https://doi.org/10.3847/1538-4365/aab766, 2018.
Mann, M. and Lees, J.: Robust Estimation of Background Noise and Signal Detection in Climatic Time Series, Clim. Change, 33, 409–445.
Other spectra:
ar1Surrogates()
,
createSyntheticTimeseries()
,
periodAnnotate()
,
plotSpectrum()
,
reverselog10_trans()
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