plot_rolcor_estim_1win | R Documentation |
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.
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)
inputdata |
The same data matrix (time, first and second variable) that was used with the |
corcoefs |
Rolling correlation coefficients estimated with the |
CRITVAL |
The critical values computed through the function |
widthwin |
|
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 |
varX, varY |
Names of the first (e.g., X) and the second (e.g., Y) variables contained in |
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 ( |
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 |
|
pchCRIVAL |
|
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).
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.
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: josue.m.polanco@gmail.com.
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>.
# 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)
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