susy | R Documentation |
Cross-correlations of two time series are computed up to a specific lag in seconds maxlag
. Cross-correlation is done within segment of the time series. The size of segments segment
can be chosen in seconds. Aggregation is then performed by transforming correlations to Fisher's Z, computing mean Z in each segment, then across all segments of the time series. Segment shuffling is used to create surrogate time series, on which the same computations are run. This provides effect sizes ES
. SUSY provides these different synchrony measures for each twin time series: mean Z
and ES of mean Z
; mean absolute_Z
and ES of mean absolute_Z
.
susy(x, segment, Hz, maxlag=3L, permutation=FALSE, restrict.surrogates=FALSE, surrogates.total=500)
x |
A data.frame of numeric columns. |
segment |
Integer, size in seconds. Must not be smaller than |
Hz |
Integer, frames per second (sampling rate). |
maxlag |
Integer, maximum lag for |
permutation |
Logical, default |
restrict.surrogates |
Logical, default |
surrogates.total |
Numeric, the number of generated surrogates, default |
Segments are non-overlapping, and the number of segments that fit into the time series may have a remainder (usually a few seconds at the end of the time series), which is not considered.
Object of class susy
is returned. Each cross correlation pair is an element in resulting object.
plot.susy
, as.data.frame.susy
, print.susy
n = 1000 data = data.frame( var1 = runif(n, 300, 330), var2 = runif(n, 300, 330), var3 = runif(n, 300, 330) ) ## use only first two columns res = susy(data[, 1:2], segment=30L, Hz=15L) length(res) names(res) ## use all columns and permutation res = susy(data, segment=30L, Hz=15L, permutation=TRUE) length(res) names(res) ## print susy res print(res, legacy=TRUE) ## plot susy plot(res) plot(res, type=1:2)
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