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Compute and plot summary statistics of cross-correlation matrices (CCM) for high-dimensional time series.
Summaryccm(x, max.lag = 12)
T by k data matrix: T data points in rows with each row being data at a given time point, and k time series in columns.
The number of lags for CCM.
A list containing:
pvalue - P-values of Chi-square tests of individual-lag CCM being zero-matrix.
ndiag - Percentage of significant diagonal elements for each lag.
noff - Percentage of significant off-diagonal elements for each lag.
# NOT RUN { data(TaiwanAirBox032017) output <- Summaryccm(as.matrix(TaiwanAirBox032017[,1:4])) # }
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