plot_estimation_heatmap: Plot the variables under analysis and a heat map of the...

In NonParRolCor: a Non-Parametric Statistical Significance Test for Rolling Window Correlation

Plot the variables under analysis and a heat map of the rolling correlation coefficients that are statistically significant

Description

The plot_rolcor_estim_heatmap function plots the time series under study and create a heat map of the rolling window correlation coefficients that are statistically significant that are obtained by the rolcor_estim_heatmap function.

Usage

plot_rolcor_estim_heatmap(inputdata, corcoefs, CRITVAL, Rwidthwin="", 
                  typewidthwin="", widthwin_1=3, widthwin_N=dim(inputdata)[1], 
                  varX="X", varY="Y", coltsX="black", coltsY="blue", LWDtsX=1, 
                  LWDtsY=1, CEXLAB=1.15, CEXAXIS=1.05)

Arguments

inputdata	The same data matrix (time, first and second variable) that was used with the rolcor_estim_heatmap function.
corcoefs	Rolling correlation coefficients estimated with the rolcor_estim_heatmap function.
CRITVAL	The critical values computed through the function rolcor_estim_heatmap and that used to determine the statistical significance.
Rwidthwin	Rwidthwin is a vector that contain the window lengths, which come from the function rolcor_estim_heatmap.
typewidthwin	Contains the type (“FULL” or “PARTIAL”) of heat map that will be plotted, this information is provided by rolcor_estim_heatmap. Please note that whether option typewidthwin=“PARTIAL” is selected, and you must provide the parameters widthwin_1 and widthwin_N to plot the heat map.
widthwin_1	First value for the size (length) of the windows when the option typewidthwin=“PARTIAL” is selected, this value is provided by the function rolcor_estim_heatmap.
widthwin_N	Last value for the size (length) of the windows when the option typewidthwin=“PARTIAL” is selected, this value is provided by the function rolcor_estim_heatmap.
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.
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.
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.

Details

The plot_rolcor_estim_heatmap function plots the variables (time series) under analysis and a heat map of the rolling correlation coefficients that are statistically significant. This function supersedes to the function heatmap_NonParRolCor of the previous version of NonParRolCor. The plot_rolcor_estim_heatmap function uses the outputs of the rolcor_estim_heatmap function. To implement this method we extend the works of Telford (2013), Polanco-Martínez (2019) and Polanco-Martínez (2020), and to implement the heat map we follow to Polanco-Martínez (2020). The test/method to determine the statistical significance is described in Polanco-Martínez and López-Martínez (2021). plot_rolcor_estim_heatmap uses the functions diverge_hcl (package:colorspace) and alpha (package:scales) to create the palette of colors.

Value

Outputs: A plot of the time series under analysis and a heat map (a multi-plot via screen) of the rolling correlation coefficients 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_rolcor_estim_heatmap"
 # Defining NonParRolCor parameters 
 TYPEWIDTHWIN="PARTIAL"
 # Number of Monte-Carlo simulations (MCSim), please use at least 1000. 
 # WARNING: MCSim=2, it's just to test this example! 
 MCSim <- 2 
 Np    <- 2  # Number of cores 
 X_Y <- rolcor_estim_heatmap(syntheticdata[1:350,], CorMethod="pearson", 
                   typewidthwin=TYPEWIDTHWIN, widthwin_1=29,
                   widthwin_N=51, Align="center", rmltrd=TRUE, 
                   Scale=TRUE, MCSim=MCSim, Np=Np)
 plot_rolcor_estim_heatmap(syntheticdata[1:350,], X_Y$matcor, X_Y$CRITVAL, 
                   Rwidthwin=X_Y$Windows, typewidthwin=TYPEWIDTHWIN, 
                   widthwin_1=29, widthwin_N=51)
 

NonParRolCor documentation built on Oct. 31, 2022, 1:06 a.m.