signal_spectrum {eseis} | R Documentation |
Calculate the spectrum of a time series
Description
The power spectral density estimate of the time series is calculated using
different approaches.
Usage
signal_spectrum(data, dt, method = "periodogram", n, res, log = FALSE, ...)
Arguments
data |
eseis object, numeric vector or list of
objects, data set to be processed.
|
dt |
Numeric value, sampling period. If omitted, dt
is set to 1/200. Only needed if data is no eseis object.
|
method |
Character value, calculation method. One out of
"periodogram" and "autoregressive" .
default is "periodogram" .
|
n |
Numeric value, optional number of samples in
running window used for smoothing the spectrogram. Only applied if a
number is provided. Smoothing is performed as running mean.
|
res |
Numeric value, optional resolution of the spectrum,
i.e. the number of power and frequency values. If omitted, the full
resolution is returned. If used, a spline interpolation is performed.
|
log |
Logical value, option to interpolate the spectrum with
log spaced frequency values. Default is FALSE .
|
... |
Additional arguments passed to the function.
|
Details
If the res
option is used, the frequency and power vectors will be
interpolated using a spline interpolator, using equally spaced frequency
values. If desired, the additional option log = TRUE
can be used
to interpolate with log spaced frequency values.
Value
Data frame
with frequency and power vector
Author(s)
Michael Dietze
Examples
## load example data set
data(rockfall)
## calculate spectrum with standard setup
s <- signal_spectrum(data = rockfall_eseis)
## plot spectrum
plot_spectrum(data = s)
[Package
eseis version 0.8.0
Index]