| sdPercentiles {dLagM} | R Documentation |
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
n |
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. |
N |
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. |
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).
Value
rollCorSd.limits |
Percentiles of MC distribution of standard deviations of rolling correlations as the test limits. |
Author(s)
Haydar Demirhan
Maintainer: Haydar Demirhan <haydar.demirhan@rmit.edu.au>
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
# sdPercentiles(n = 50, cor = 0.5, width = 5, N = 50,
# percentiles = c(.025, .975))