plot_rolcor_estim_1win {NonParRolCor}

Plot the variables under analysis and the rolling correlation coefficients that are statistically significant for only one window-length

Description

The plot_rolcor_estim_1win function plots the time series under study and create a simple plot of the rolling window correlation coefficients that are statistically significant that are obtained by the rolcor_estim_1win function.

Usage

plot_rolcor_estim_1win(inputdata, corcoefs, CRITVAL, widthwin, left_win,
                  righ_win, varX="X", varY="Y", coltsX="black", coltsY="blue", 
                  rmltrd=TRUE, Scale=TRUE, HeigWin1=2.05, HeigWin2=2.75, 
                  colCOEF="black", CEXLAB=1.15, CEXAXIS=1.05, LWDtsX=1, 
                  LWDtsY=1, LWDcoef=1, colCRITVAL="black", pchCRIVAL=16) 

Arguments

inputdata

The same data matrix (time, first and second variable) that was used with the rolcor_estim_1win function.

corcoefs

Rolling correlation coefficients estimated with the rolcor_estim_1win function.

CRITVAL

The critical values computed through the function rolcor_estim_1win, which are used to determine the statistical significance.

widthwin

widthwin contains the window-length (time-scale) that come from the function rolcor_estim_1win.

left_win, righ_win

These parameters are used to accommodate (to the left and right) the times of the rolling window correlation coefficients and these are provided by the rolcor_estim_1win function.

varX, varY

Names of the first (e.g., X) and the second (e.g., Y) variables contained in inputdata. Please note that the names of these two variables should be provided (by default are X and Y) when these variables are plotted.

coltsX, coltsY

Colors to be used when the variables are plotted, by default are “black” for the first variable and “blue” for the second, but other colors can be used.

rmltrd

Remove (by default is “TRUE”; “FALSE” otherwise) the linear trend in the variables under analysis. It is advisable to remove the trend before estimating the rolling window correlation coefficients, especially, for large window-lengths.

Scale

Scale (by default is “TRUE”; “FALSE” otherwise) is used to “normalize” or “standardize” the variables under analysis. It is highly advisable to ”normalize/standardize” the time series under study to have them in the same scales.

HeigWin1, HeigWin2

Proportion of window's size to plot the time series under analysis (HeigWin1) and the rolling window correlation coefficients (HeigWin2) (look at: R>?layout to get more information about “layout”). By default HeigWin1 and HeigWin2 have values of 2.05 and 2.75, but other values can be used.

colCOEF

The color to be used when the correlation coefficients are plotted, by default the color is “black”, but other colors can be used.

CEXLAB, CEXAXIS

These parameters are used to plot the sizes of the X-axis and Y-axis labels and X- and Y-axis, by default these parameters have values of 1.15 and 1.05, respectively, but it is possible to use other values.

LWDtsX, LWDtsY

Line-widths for the first and the second variable when these are plotted, by default these have values of 1, but other values (widths) can be used.

LWDcoef

The line-width to be used when the correlation coefficients are plotted, by default this parameter has a value of 1, but it is possible to use other values.

colCRITVAL

colCRITVAL defines the color used to plot the correlation coefficients that are statistically significant.

pchCRIVAL

pchCRITVAL is used for the type (symbols) of the plot for the correlation coefficients that are statistically significant.

Details

The plot_rolcor_estim_1win function plots the variables (time series) under analysis and for the selected window-length, the rolling correlation coefficients that are statistically significant, which are estimated through a non-parametric computing-intensive method. The plot_rolcor_estim_1win function uses the outputs of rolcor_estim_1win. To implement this method we extend the works of Telford (2013), Polanco-Martínez (2019) and Polanco-Martínez (2020), and to implement the simple plot we follow to Polanco-Martínez (2020). The test/method to determine the statistical significance is described in Polanco-Martínez and López-Martínez (2021).

Value

Outputs: A plot of the time series under analysis, and for the selected window-length, the rolling window correlation coefficients that are statistically significant. This multi-plot can be saved in your preferred format.

Author(s)

Josué M. Polanco-Martínez (a.k.a. jomopo).
Excellence Unit GECOS, IME, Universidad de Salamanca, Salamanca, SPAIN.
BC3 - Basque Centre for Climate Change, Leioa, SPAIN.
Web1: https://scholar.google.es/citations?user=8djLIhcAAAAJ&hl=en/.
Web2: https://www.researchgate.net/profile/Josue-Polanco-Martinez/.
Email: [e-mail].

References

Polanco-Martínez, J. M. and López-Martínez, J.M. (2021). A non-parametric method to test the statistical significance in rolling window correlations, and applications to ecological time series. Ecological Informatics, 60, 101379. <URL: doi: 10.1016/j.ecoinf.2021.101379>.

Polanco-Martínez, J. M. (2020). NonParRolCor: an R package for estimating rolling window multiple correlation in ecological time series. Ecological Informatics, 60, 101163. <URL: doi: 10.1016/j.ecoinf.2020.101163>.

Examples

 # Code to test the function "plot_rolcol_estim_1win"
 # Defining NonParRolCor parameters 
 MCSim <- 2 
 Np    <- 2
 X_Y <- rolcor_estim_1win(as.matrix(syntheticdata[1:350,]), 
                   CorMethod="pearson", widthwin=21, Align="center", 
                   rmltrd=TRUE, Scale=TRUE, MCSim=MCSim, Np=Np, prob=0.95)
 plot_rolcor_estim_1win(syntheticdata[1:350,], 
                   corcoefs=X_Y$Correlation_coefficients, 
                   CRITVAL=X_Y$CRITVAL, widthwin=X_Y$widthwin, 
                   left_win=X_Y$left_win, righ_win=X_Y$righ_win)