plot_1win {RolWinMulCor}

Plot the outputs of rolwincor_1win and rolwinmulcor_1win as a single one window

Description

The plot_1win function plots the time series under study and the correlation coefficients and their respective p-values (corrected or not corrected) as only one selected window-length (time-scale) using the outputs of the functions rolwincor_1win (bi-variate case) or rolwinmulcor_1win (multi-variate case). The plot_1win function is highly flexible since this contains several parameters to control the plot output. We would highlight that only the first 11 parameters must be defined by the users, the other parameters are not strictly necessary since these have been defined by default. A list of parameters are described in the following lines.

Usage

plot_1win(inputdata, corcoefs, pvalues, left_win, righ_win, widthwin, 
          KCASE="", varX="", varY="", coltsX=c("black"), coltsY="blue", 
          rmltrd=TRUE, Scale=TRUE, HeigWin1=2.05, HeigWin2=2.75, 
          colCOEF="black", colPVAL="gray", CEXLAB=1.15, CEXAXIS=1.05, 
          LWDtsX=c(1), LWDtsY=1, LWDcoef=1, LWDpval=1, NUMLABX=5, 
          parcen=c(0.5,25)) 

Arguments

inputdata

Input data used in the functions rolwincor_1win or rolwinmulcor_1win.

corcoefs, pvalues

Correlation coefficients obtained from the functions rolwincor_1win or
rolwinmulcor_1win (named Correlation_coefficients) and p-values obtained from the aforementioned functions (named P_values_corrected or
P_values_not_corrected).

left_win, righ_win

These parameters are used to accommodate the times in the rolling window correlations and are obtained from the functions rolwincor_1win or
rolwinmulcor_1win, which have the same names.

widthwin

Window size to compute the rolling window correlations. This value can be an even or odd number of at least three (the default value), and this parameter is the same as the one used in rolwincor_1win or rolwinmulcor_1win.

KCASE

This parameter is used to activate the cases: “BIVAR” for the bi-variate or “MULVAR” for the multi-variate, and this must be the same as the one used in rolwincor_1win or rolwinmulcor_1win.

varX

Name of the “first” or independent variable, e.g. “X” (please note that “X” is a vector of one element if KCASE=“BIVAR” and a vector of several elements if KCASE=“MULVAR”). For the multi-variate case the names for “X” (the independent variables) will be defined as: varX=paste(“X1”, “X2”,..., sep=”, ”).

varY

Name of the “second” or dependent variable, e.g. “Y”.

coltsX, coltsY

Colors to be used when the variables are plotted, for the bi-variate case by default are “black” for “X” and “blue” for “Y”, but other colors can be used. For the multi-variate case, colors for the dependent (“Y”) and independent variables (“X”) MUST be provided (e.g. coltsX=c("red","blue",...), coltsY="black").

rmltrd

Remove (by default is “TRUE”; “FALSE” otherwise) the linear trend in the time series under analysis.

Scale

Scale (by default is “TRUE”; “FALSE” otherwise) is used to “normalize” or “standardize” the time series under analysis.

HeigWin1, HeigWin2

Proportion of window's size to plot the time series under analysis (HeigWin1) and the rolling window correlation coefficients and p-values (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, colPVAL

The colors to be used when the correlation coefficients and their corresponding p-values are plotted, by default the colors are “black” and “gray,” 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, for the bi-variate case by default these have values of 1, but other values (widths) can be used. For the multi-variate case and for the independent variables the line-widths MUST be provided (e.g. LWDtsX = c(1,2,...)).

LWDcoef, LWDpval

The line-widths to be used when the correlation coefficients and their respective p-values are plotted, by default these parameters have a value of 1, but it is possible to use other values.

NUMLABX

Number of labels for (all) the X's axis, by the default is 5, but it is possible to use other values.

parcen

These parameters contain two values: the first one is to control the position of the title, by default it is 0.5, but you should try with other close values to obtain the title centered, e.g. 0.4 or 0.8 (please avoid to use large values); the second value is to define the spaces between the names of variables, by default is 25 spaces, but you could try other values to fit properly the names of variables in the title. We use “mtext” to produce the title (please loot at R>?mtext for more information).

Details

The plot_1win function plots the correlation coefficients and their respective p-values (corrected or not corrected) as only one selected window-length using the outputs of the functions rolwincor_1win (bi-variate case) and rolwinmulcor_1win (multi-variate case).

Value

Output: a single plot (via screen) of the correlation coefficients and their respective (corrected and not corrected) p-values.

Author(s)

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

References

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: doi: 10.1111/j.2517-6161.1995.tb02031.x>.

Polanco-Martínez, J. M. (2019). Dynamic relationship analysis between NAFTA stock markets using nonlinear, nonparametric, non-stationary methods. Nonlinear Dynamics, 97(1), 369-389. <URL: doi: 10.1007/s11071-019-04974-y>.

Polanco-Martínez, J. M. (2020). RolWinMulCor : 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>.

Telford, R.: Running correlations – running into problems (2013). <URL:
https://quantpalaeo.wordpress.com/2013/01/04/>.

Examples

#######################################################################
# Testing the bi-variate case (1 window), synthetic data!
#######################################################################
# Fig. 3 in Polanco-Martínez (2020) 
test_fun1 <- rolwincor_1win(syntDATA, varX="X", varY="Y", 
                            CorMethod="spearman", widthwin=21, 
                            Align="center", pvalcorectmethod="BH")
# Plotting the bi-variate case (1 window)
plot_1win(syntDATA, test_fun1$Correlation_coefficients, 
         test_fun1$P_values_corrected, test_fun1$left_win, 
         test_fun1$righ_win, widthwin=21, KCASE="BIVAR", 
         varX="X", varY="Y")
#######################################################################
# Testing the multi-variate case (1 window), real-life ecological data! 
#######################################################################
# Fig. 6 in Polanco-Martínez (2020)
test_fun2 <- rolwinmulcor_1win(YX_ecological_data, widthwin=61,
                            Align="center", pvalcorectmethod="BH")
# Plotting the bi-variate case (1 window), real-life ecological data 
plot_1win(YX_ecological_data, test_fun2$Correlation_coefficients, 
          test_fun2$P_values_corrected, test_fun2$left_win, 
          test_fun2$righ_win, widthwin=61, KCASE="MULVAR", varY="PC1", 
          varX=paste("SST", "TSI", sep=", "), coltsY="black", 
          coltsX=c("red", "orange"), CEXLAB=1.15, CEXAXIS=1.65, 
          LWDtsX=rep(2,2), LWDtsY=2, parcen=c(0.45,15))