ecodata2

<p>The data set <code>ecodata2</code> contains three columns, the first one (named ''Years'') is the time (years from 1700 to 1936, yearly resolution), the second column (named ''TSI'') are reconstructions of total solar irradiance (Lean 2000) and the third column the first component principal (PC1) of the reconstructed Atlantic Bluefin Tuna (BFT) captures (Ganzedo et al. 2016, Polanco-Martínez et al. 2018).</p>

dataset3

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

ecodata2 function

<p>One file in ASCII format containing three columns and 237 rows, columns are separated by spaces.</p>

ecodata2: Environmental data set to exemplify the use of the functions contained in NonParRolCor

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