sdPercentiles: Test the significance of signal from rolling correlation analysis

Description

Implements the approach of Gershunov et al. (2001) to test the significance of signal from rolling correlation analysis.

Usage

sdPercentiles(n = 150, cor = 0.5, width = 5, N = 500, 
              percentiles = c(.05, .95))

Arguments

The length of the series in the rolling correlation analysis.

cor

The magnitude of raw correaltion betweeen two time series in the rolling correlation analysis.

width

Window length of the rolling correlation analysis.

Number of Monte Carlo replications for simulations.

percentiles

Percentiles to be reported for the Monte Carlo distribution of standard deviations of rolling correlations for the given window width.

Value

rollCorSd.limits

Percentiles of MC distribution of standard deviations of rolling correlations as the test limits.

Details

N samples of correlated white noise series are generated with a magnitude of cor; rolling correlations analysis is applied with the window length of width; Monte Carlo distribution of standard deviations of rolling correlations are generated; and desired percentiles of the MC distribution of standard deviations are reported (Gershunov et al. 2001).

References

Gershunov, A., Scheider, N., Barnett, T. (2001). Low-Frequency Modulation of the ENSO-Indian Monsoon Rainfall Relationship: Signal or Noise? Journal of Climate, 14, 2486 - 2492.

Examples

Run this code

# NOT RUN {
sdPercentiles(n = 50, cor = 0.5, width = 5, N = 100, 
              percentiles = c(.025, .975))
# }

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