EstimateCI | R Documentation |
This function estimates based on a parametric bootstrapping procedure (see Details) confidence intervals for a set of signal, noise and SNR spectra obtained from an actual proxy record array.
EstimateCI(
spectra,
f.start = NULL,
f.end = NULL,
nmc = 10,
probs = c(0.1, 0.9),
df.log = NULL,
ci.df.log = NULL
)
spectra |
a list with the spectral objects |
f.start |
lower end of the frequency range on which the power-law fit is
made on the proxy data (see Details); the default |
f.end |
as |
nmc |
integer; the number of replications for the confidence interval estimation. |
probs |
length-2 numeric vector of probabilities with values in [0,1] defining the confidence interval; defaults to the 10-90 % interval. |
df.log |
width of the Gaussian kernel in logarithmic frequency units to
smooth the spectral estimates of the simulated data; some smoothing is
usually necessary to avoid physically implausible negative power
occasionally occuring for some frequencies upon estimating the common
signal spectrum. It is suggested to use the same amount of smoothing as for
the actual proxy data, while setting |
ci.df.log |
width of the Gaussian smoothing kernel to smooth the
estimated confidence intervals, merely for visual purposes; |
The parametric bootstrapping procedure for the confidence level estimation is
implemented as follows. A power-law fit of the form alpha * f^(-beta)
is applied to the actual signal and noise spectra, and the resulting
power-law coefficients are used to generate surrogate signal and noise series
in a simulated array that mimics the actual proxy record array. This
simulated array is replicated nmc
times and lower and upper quantiles
are calculated across the realizations of the signal, noise and SNR
surrogates. Subsequently, the quantiles are scaled to the respective mean
estimates and applied multiplicatively to the actual proxy estimates of
signal, noise, and SNR to yield the confidence intervals.
the input spectra
object, amended by the confidence intervals
for the signal, noise and SNR spectra (element lim.1
gives the upper
confidence level, element lim.2
the lower level, respectively).
Thomas Münch
SeparateSignalFromNoise
,
ObtainArraySpectra
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