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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

see WaveletComp::analyze.wavelet().

method

see WaveletComp::analyze.wavelet().

params

see WaveletComp::analyze.wavelet().

n.sim

number of simulations (default 1).

date.format

see WaveletComp::analyze.wavelet().

date.tz

see WaveletComp::analyze.wavelet().

verbose

see WaveletComp::analyze.wavelet().

Value

an analyze.wavelet object.

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... 
#> 
  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
#> 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