'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 environmental data (although this can be applied to other kinds of data sets). 'RolWinMulCor' is based on the concept of rolling or running window correlation and is useful to evaluate the evolution of correlation through time and time-scales. 'RolWinMulCor' contains four functions: (1) the first two are focused on the bi-variate case, one of them produces a simple plot of correlation coefficients and p-values (<=0.05) for only one window-length (time-scale), and the other function produces a heat map for the statistically significant (p-values <=0.05) correlation coefficients taking into account all the possible window-lengths (which are determined by the number of elements of the time series under analysis) or for a band of window-lengths; (2) the second two functions are designed to analyse the multi-variate case and follows the bi-variate case to display visually the results although these two approaches are methodologically different. The four functions contained in 'RolWinMulCor' are highly flexible since this contains a great number of 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 output (e.g. this can be displayed in the screen or can be saved as PNG, JPG, EPS or PDF formats). The 'RolWinMulCor' package also provides examples with synthetic and real environmental time series to exemplify its use. We would like to highlight that, to the best of our knowledge, there are few R packages (probably the one) on CRAN that estimate rolling window correlation and produce a heat map for the bi-variate case and, especially, for the multi-variate case.
| Package: | RolWinMulCor |
| Type: | Package |
| Version: | 0.4 |
| Date: | 2020-05-21 |
| License: | GPL (>= 2) |
| LazyLoad: | yes |
RolWinMulCor package contains four functions: (1) rolwincor_1win (estimates and plots the rolling window correlation for the bi-variate case for only one window-length or time-scale for the time series under study), (2) rolwincor_heatmap (estimates and plots as a heat map the statistically significant (p-values <=0.05) correlation coefficients taking into account all the possible window-lengths that are determined by the number of elements of the time series under or a band of window-lengths and plot the the correlation coefficients and their respective p-values as a heat map), (3) rolwinmulcor_1win (estimates and plots the rolling window correlation for the multi-variate case for only one window-length or time-scale for the time series under study) and, (4) rolwinmulcor_heatmap (estimates and plots the heat map for the multi-variate case). The bi-variate case follow from a methodological point of view to Telford (2013) and Polanco-Mart<U+00ED>nez (2019) whereas the multi-variate case follow to Abdi (2007).
Abdi H. Multiple correlation coefficient, in Encyclopedia of Measurement and Statistics, N. J. Salkind, Ed. Sage, Thousand Oaks, CA, USA, 2007; 648-651. <URL: https://personal.utdallas.edu/~herve/Abdi-MCC2007-pretty.pdf>.
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57 (1), 289-300. <URL: https://rss.onlinelibrary.wiley.com/doi/10.1111/j.2517-6161.1995.tb02031.x>.
Polanco-Mart<U+00ED>nez, J. M. (2019). Dynamic relationship analysis between NAFTA stock markets using nonlinear, nonparametric, non-stationary methods. Nonlinear Dynamics, 97(1), 369-389. <URL: https://doi.org/10.1007/s11071-019-04974-y>.
Telford, R.: Running correlations -- running into problems (2013). <URL: https://quantpalaeo.wordpress.com/2013/01/04/running-correlations-running-into-problems/>.