Computes the oscillation score, generates a surrogate distribution, and returns the log-Z score: $$Z = (log(O) - mean(log(O_{rep}))) / sd(log(O_{rep}))$$.
Usage
oscillation_score_z(
signal,
fs,
flim,
nrep = 500,
alpha = 0.05,
keep_trend = FALSE,
trend_dist = c("gamma"),
trend_ddt = NULL,
trend_alpha = 0.05,
surrogate_method = c("auto", "event_shuffle", "event_trend", "phase_randomized"),
signal_mode = c("auto", "event", "continuous"),
fcor = FALSE,
taper = c("none", "hann", "hanning"),
ci_nboot = 0,
ci_level = 0.95,
tidy = FALSE,
...
)
oscore_z(...)Arguments
- signal
numeric vector (binary or continuous).
- fs
sampling rate in Hz.
- flim
length-2 numeric vector
c(fmin, fmax)in Hz.- nrep
number of surrogates.
- alpha
significance level for one-tailed Z-thresholding.
- keep_trend, trend_dist, trend_ddt, trend_alpha
passed to surrogates.
- surrogate_method, signal_mode
passed to
oscillation_score_surrogates.- fcor
logical; apply 1/f correction.
- taper
taper before FFT.
- ci_nboot
number of bootstrap draws for the log-Z confidence interval. Set to 0 to skip CI estimation.
- ci_level
confidence level for bootstrap CI.
- tidy
logical; if TRUE return a one-row data.frame.
- ...
further arguments passed to
oscillation_score.