Analyze Wavelet from View object
analyze_wavelet.Rd
Analyze Wavelet from View object
Usage
analyze_wavelet(
obj,
column,
loess.span = 0,
dj = 1/20,
lowerPeriod = 2/obj$recording$fps,
upperPeriod = 5,
make.pval = TRUE,
method = "white.noise",
params = NULL,
n.sim = 1,
date.format = NULL,
date.tz = NULL,
verbose = TRUE
)
Arguments
- obj
View object.
- column
Column in view to analyse.
- loess.span
parameter alpha in loess controlling the degree of time series smoothing, if the time series is to be detrended; no detrending if loess.span = 0. Default: 0.
- dj
frequency resolution. Default 1/20.
- lowerPeriod
lower Fourier period in seconds. Defaults to 2/fps.
- upperPeriod
upper Fourier period in seconds. Defaults to 5s.
- make.pval
- method
- params
- n.sim
number of simulations (default 1).
- date.format
- date.tz
- verbose
See also
Other wavelet functions:
analyze_coherency()
,
get_local_max_average_power()
,
plot_average_coherency()
,
plot_average_power()
,
plot_cross_spectrum()
,
plot_cwt_energy()
,
plot_phase_difference()
,
plot_power_spectrum()
,
plot_roll_resultant_length()
,
plot_sel_phases()
,
plot_wt_energy()
Examples
r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
w <- analyze_wavelet(pv, "Nose_y")
#> Starting wavelet transformation...
#> ... and simulations...
#>
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#> Class attributes are accessible through following names:
#> series loess.span dt dj Wave Phase Ampl Power Power.avg Power.pval Power.avg.pval Ridge Period Scale nc nr coi.1 coi.2 axis.1 axis.2 date.format date.tz