Spectrum {s2dv}

Estimate frequency spectrum

Description

Estimate the frequency spectrum of the data array together with a user-specified confidence level. The output is provided as an array with dimensions c(number of frequencies, stats = 3, other margin dimensions of data). The 'stats' dimension contains the frequencies at which the spectral density is estimated, the estimates of the spectral density, and the significance level.
The spectrum estimation relies on an R built-in function spectrum() and the confidence interval is estimated by the Monte-Carlo method.

Usage

Spectrum(data, time_dim = "ftime", alpha = 0.05, ncores = NULL)

Arguments

data

A vector or numeric array of which the frequency spectrum is required. If it's a vector, it should be a time series. If it's an array, the dimensions must have at least 'time_dim'. The data is assumed to be evenly spaced in time.

time_dim

A character string indicating the dimension along which to compute the frequency spectrum. The default value is 'ftime'.

alpha

A numeric indicating the significance level for the Monte-Carlo significance test. The default value is 0.05.

ncores

An integer indicating the number of cores to use for parallel computation. The default value is NULL.

Value

A numeric array of the frequency spectrum with dimensions c(<time_dim> = number of frequencies, stats = 3, the rest of the dimensions of 'data'). The 'stats' dimension contains the frequency values, the spectral density, and the confidence interval.

Examples

# Load sample data as in Load() example:
example(Load)
ensmod <- MeanDims(sampleData$mod, 2)
spectrum <- Spectrum(ensmod)

for (jsdate in 1:dim(spectrum)['sdate']) {
 for (jlen in 1:dim(spectrum)['ftime']) {
   if (spectrum[jlen, 2, 1, jsdate] > spectrum[jlen, 3, 1, jsdate]) {
     ensmod[1, jsdate, ] <- Filter(ensmod[1, jsdate, ], spectrum[jlen, 1, 1, jsdate])
   }
 }
}
 
PlotAno(InsertDim(ensmod, 2, 1), sdates = startDates)