YX_ecological_data

The data set <code><a rd-options="" href="/link/YX_ecological_data?package=RolWinMulCor&version=1.2.0" data-mini-rdoc="RolWinMulCor::YX_ecological_data">YX_ecological_data</a></code> contains four columns, the first one (``Years'') is the time (years from 1700 to 1936), the second is the first component principal (``PC1'') of the reconstructed Atlantic Bluefin Tuna (BFT) captures (Ganzedo et al., 2016, Polanco-Mart&lt;U+00ED&gt;nez et al., 2018), the third are reconstructions of sea surface temperature (``SST'') from the Northern Hemisphere (NH) (Mann et al. 2009), and the fourth column contains reconstructions of total solar irradiance (``TSI'') (Lean 2000).

dataset2

Rolling Window Multiple Correlation ('RolWinMulCor') estimates the rolling (running) window correlation for the bi- and multi-variate cases between regular (sampled on identical time points) time series, with especial emphasis to ecological data although this can be applied to other kinds of data sets. 'RolWinMulCor' is based on the concept of rolling, running or sliding window and is useful to evaluate the evolution of correlation through time and time-scales. 'RolWinMulCor' contains six functions. The first two focus on the bi-variate case: (1) rolwincor_1win() and (2) rolwincor_heatmap(), which estimate the correlation coefficients and the their respective p-values for only one window-length (time-scale) and considering all possible window-lengths or a band of window-lengths, respectively. The second two functions: (3) rolwinmulcor_1win() and (4) rolwinmulcor_heatmap() are designed to analyze the multi-variate case, following the bi-variate case to visually display the results, but these two approaches are methodologically different. That is, the multi-variate case estimates the adjusted coefficients of determination instead of the correlation coefficients. The last two functions: (5) plot_1win() and (6) plot_heatmap() are used to represent graphically the outputs of the four aforementioned functions as simple plots or as heat maps. The functions contained in 'RolWinMulCor' are highly flexible since these contains several parameters to control the estimation of correlation and the features of the plot output, e.g. to remove the (linear) trend contained in the time series under analysis, to choose different p-value correction methods (which are used to address the multiple comparison problem) or to personalise the plot outputs. The 'RolWinMulCor' package also provides examples with synthetic and real-life ecological time series to exemplify its use. Methods derived from H. Abdi. (2007) <https://personal.utdallas.edu/~herve/Abdi-MCC2007-pretty.pdf>, R. Telford (2013) <https://quantpalaeo.wordpress.com/2013/01/04/, J. M. Polanco-Martinez (2019) <doi:10.1007/s11071-019-04974-y>, and J. M. Polanco-Martinez (2020) <doi:10.1016/j.ecoinf.2020.101163>.

Josue Polanco-Martinez

RolWinMulCor

Subroutines to Estimate Rolling Window Multiple Correlation

Josue M. Polanco-Martinez

YX_ecological_data function

One file in ASCII format and columns are separated by spaces.

Format

The data set <code><a rd-options='' href='YX_ecological_data'>YX_ecological_data</a></code> contains four columns, the first one (``Years'') is the time (years from 1700 to 1936), the second is the first component principal (``PC1'') of the reconstructed Atlantic Bluefin Tuna (BFT) captures (Ganzedo et al., 2016, Polanco-Mart&lt;U+00ED&gt;nez et al., 2018), the third are reconstructions of sea surface temperature (``SST'') from the Northern Hemisphere (NH) (Mann et al. 2009), and the fourth column contains reconstructions of total solar irradiance (``TSI'') (Lean 2000).

YX_ecological_data: Ecological data set to test the functions of RolWinMulCor

Description

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