get_power_spectra: Compute Power Spectra With Optional Subject-Level Aggregation

In EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice

Compute Power Spectra With Optional Subject-Level Aggregation

Description

Computes power spectral density estimates using spectrum, optionally aggregated across subjects or posterior predictive samples. All arguments intended for the underlying spectral estimator should be supplied through spectrum.args.

Usage

get_power_spectra(data, by.postn = FALSE, spectrum.args = list())

Arguments

data	A data frame with reaction time data. Must contain subjects and rt, and for posterior predictive data optionally postn and trials.
by.postn	Logical. If TRUE, compute a separate spectrum for each posterior predictive draw and each posterior sample index.
spectrum.args	A named list of arguments passed directly to spectrum. These override the defaults internally used in this function. Useful for customizing smoothing spans, detrending, tapering, and so on. Defaults: list(spans=c(3, 5), detrend=FALSE, demean=TRUE, log=FALSE, taper=0). By default, we run spectrum without log, and log-transform while plotting

Details

The function organizes the data by subject (and optionally posterior sample index), computes spectra individually, interpolates spectra to a common frequency grid if needed, and averages them appropriately.

Value

Either a data frame with columns freq and power, or (if by.postn = TRUE) a list with frequency vector and a matrix of spectra across posterior samples.

EMC2 documentation built on Jan. 12, 2026, 5:07 p.m.