syntheticdata

<p>The data set <code>syntheticdata</code> contains three columns: the first one are the ``times'' (from 1 to 500) (named ``Times''), the second (named ``X'') and the third (named ``Y'') columns were generated by a bi-variate AR1 process with similar autocorrelation coefficients of 0.25. We generate two correlated bi-variate AR1 time series with positive (direct) correlation (0.85) for the first 250 elements and with negative (inverse) correlation (-0.85) for the last 250 elements (Polanco-Martínez and López-Martínez 2021).</p>

synthetic_dataset

Estimates and plots (as a single plot and as a heat map) the rolling window correlation coefficients between two time series and computes their statistical significance, which is carried out through a non-parametric computing-intensive method. This method addresses the effects due to the multiple testing (inflation of the Type I error) when the statistical significance is estimated for the rolling window correlation coefficients. The method is based on Monte Carlo simulations by permuting one of the variables (e.g., the dependent) under analysis and keeping fixed the other variable (e.g., the independent). We improve the computational efficiency of this method to reduce the computation time through parallel computing. The 'NonParRolCor' package also provides examples with synthetic and real-life environmental time series to exemplify its use. Methods derived from R. Telford (2013) <https://quantpalaeo.wordpress.com/2013/01/04/> and J.M. Polanco-Martinez and J.L. Lopez-Martinez (2021) <doi:10.1016/j.ecoinf.2021.101379>.

Josue Polanco-Martinez

NonParRolCor

a Non-Parametric Statistical Significance Test for Rolling
Window Correlation

Josue M. Polanco-Martinez

Jose L. Lopez-Martinez

syntheticdata function

<p>One file in ASCII format containing three columns and 500 rows, columns are separated by spaces.</p>

syntheticdata: Synthetic data set to exemplify the use of the functions contained in NonParRolCor

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