rolCorPlot | R Documentation |
Plots the rolling correlations along with other required statistics to visualise the approach of Gershunov et al. (2001) to test the significance of signal from rolling correlation analysis.
rolCorPlot(x , y , width, level = 0.95, main = NULL,
SDtest = TRUE, N = 500)
x |
A ts object. |
y |
A ts object. |
width |
A numeric vector of window lengths of the rolling correlation analysis. |
level |
Confidence level for intervals. |
main |
The main title of the plot. |
SDtest |
Set to |
N |
An integer showing the number of series to be generated in Monte Carlo simulation. |
rolCor |
A matrix showing rolling correlations for each |
rolcCor.avr.filtered |
A vector showing average rolling correlations filtered by running median nonlinear filter against outliers. |
rolcCor.avr.raw |
A vector showing unfiltered average rolling correlations. |
rolCor.sd |
A vector showing standard deviations of rolling correlations for each |
rawCor |
Pearson correlation between two series. |
sdPercentiles |
Percentiles of MC distribution of standard deviations of rolling correlations as the test limits. |
test |
A data frame showing the standard deviations of rolling correlations for each |
Haydar Demirhan
Maintainer: Haydar Demirhan <haydar.demirhan@rmit.edu.au>
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.
## Not run:
data(wheat)
prod.ts <-ts(wheat[,5], start = 1960)
CO2.ts <- ts(wheat[,2], start = 1960)
rolCorPlot(x = prod.ts, y = CO2.ts , width = c(7, 11, 15), level = 0.95, N = 50)
## End(Not run)
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