rdata_climate

<p>The data set <code>rdata_climate</code> contains four columns: the first one (named 'Time') are years from 500 to 1850, the following three columns are the MDRSST (Sea Surface Temperatures anomalies from the Main Developed Region for tropical cyclones) (Mann et al. 2009a), the ENSO (El Niño-Southern Oscillation SST anomalies, el Niño 3 region) (Mann et al. 2009b) and the AMO (The North Atlantic Multidecadal Oscillation SST anomalies) (Mann et al. 2009b).</p>

rdataset_climate

Estimates and plots as a heat map the correlation coefficients obtained via the wavelet local multiple correlation 'WLMC' (Fernández-Macho 2018) and the 'dominant' variable/s, i.e., the variable/s that maximizes the multiple correlation through time and scale (Polanco-Martínez et al. 2020, Polanco-Martínez 2022). We improve the graphical outputs of WLMC proposing a didactic and useful way to visualize the 'dominant' variable(s) for a set of time series. The WLMC was designed for financial time series, but other kinds of data (e.g., climatic, ecological, etc.) can be used. The functions contained in 'VisualDom' are highly flexible since these contains several parameters to personalize the time series under analysis and the heat maps. In addition, we have also included two data sets (named 'rdata_climate' and 'rdata_Lorenz') to exemplify the use of the functions contained in 'VisualDom'. Methods derived from Fernández-Macho (2018) <doi:10.1016/j.physa.2017.11.050>, Polanco-Martínez et al. (2020) <doi:10.1038/s41598-020-77767-8> and Polanco-Martínez (2023, in press).

Josue Polanco-Martinez

VisualDom

Visualize Dominant Variables in Wavelet Multiple Correlation

Josué M. Polanco-Martínez

rdata_climate function

<p>One file in ASCII format containing 4 columns and 1350 rows, columns are separated by spaces.</p>

rdata_climate: Climate data set to exemplify the use of the functions contained in VisualDom

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