Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/func_pspectrum.R
This is the primary function to be used in this package: it returns power spectral density estimates of a univariate timeseries, with an optimal number of tapers at each frequency based on iterative reweighted spectral derivatives.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | pspectrum(x, ...)
## S3 method for class 'ts'
pspectrum(x, ...)
## S3 method for class 'spec'
pspectrum(x, ...)
## Default S3 method:
pspectrum(x, x.frqsamp = 1, ntap.init = NULL, niter = 5,
AR = FALSE, Nyquist.normalize = TRUE, verbose = TRUE,
no.history = FALSE, plot = FALSE, ...)
pspectrum_basic(x, ntap.init = 7, niter = 5, verbose = TRUE, ...)
adapt_message(stage, dvar = NULL)
|
x |
vector; series to find PSD estimates for |
... |
Optional parameters passed to |
x.frqsamp |
scalar; the sampling rate (e.g. Hz) of the series |
ntap.init |
scalar; the number of sine tapers to use in the pilot spectrum estimation; if |
niter |
scalar; the number of adaptive iterations to execute after the pilot spectrum is estimated. |
AR |
logical; should the effects of an AR model be removed from the pilot spectrum? |
Nyquist.normalize |
logical; should the units be returned on Hz, rather than Nyquist? |
verbose |
logical; Should messages be given? |
no.history |
logical; Should the adaptive history not be saved? |
plot |
logical; Should the results be plotted? |
stage |
integer; the current adaptive stage (0 is pilot) |
dvar |
numeric; the spectral variance; see also |
See the Adaptive estimation section in the description of
the psd-package
for details regarding adaptive estimation.
pspectrum_basic
is a simplified implementation used mainly for
testing.
Object with class 'spec', invisibly. It also assigns the object to
"final_psd"
in the working environment.
A.J. Barbour adapted original by R.L. Parker
psdcore
, pilot_spec
, riedsid
, prewhiten
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ## Not run: #REX
library(psd)
library(RColorBrewer)
##
## Adaptive multitaper PSD estimation
## (see also the "psd_overview" vignette)
##
data(magnet)
Xr <- magnet$raw
Xc <- magnet$clean
# adaptive psd estimation (turn off diagnostic plot)
PSDr <- pspectrum(Xr, plot=FALSE)
PSDc <- pspectrum(Xc, plot=FALSE)
# plot them on the same scale
plot(PSDc, log="dB",
main="Raw and cleaned Project MAGNET power spectral density estimates",
lwd=3, ci.col=NA, ylim=c(0,32), yaxs="i")
plot(PSDr, log="dB", add=TRUE, lwd=3, lty=5)
text(c(0.25,0.34), c(11,24), c("Clean","Raw"), cex=1)
## Change sampling, and inspect the diagnostic plot
plot(pspectrum(Xc, niter=1, x.frqsamp=10, plot=TRUE))
## Say we forgot to assign the results: we can recover from the environment with:
PSDc_recovered <- psd_envGet("final_psd")
plot(PSDc_recovered)
## End(Not run)#REX
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