# Plot as a heat map the correlation coefficients contained in the output of the function 'estim_WLMC' The `plot_estim_WLMC` function plots as a heat map the output of the `estim_WLMC` function. One of the features of `plot_estim_WLMC` is that this function discern the correlation coefficients that are not statistically significant, which use blanks to plot these coefficients. ```r plot_estim_WLMC(inputdata, LISTvals, J, fac=1, FLAG=TRUE, FLAGNA=1, COLS=c(1:5), LTY=c(rep(1,5)), LWD=c(rep(1.2,5)), DIST=c(seq(0,10,2))) ``` ## Arguments - `inputdata`: A matrix of N columns by P rows: the first column is time (regular/evenly spaced) and the other columns are the variables under study. - `LISTvals`: Output of the function `estim_WLMC`. This is a list that contains: (1) CORCOEF (correlation coefficients), (2) CIlo (CI lower bounds ), (3) CIup (CI upper bounds), and (4) YmaxR (the index numbers, from 1 to number of variables, of the variable whose correlation is calculated against a linear combination of the rest). - `J`: The maximum level of the MODWT decomposition. It is recommended to use $log2(N) - 3$, where N is the number of rows or elements of `inputdata` (Fernández-Macho 2018, Polanco-Martínez et al. 2020). - `fac`: This factor is used to scale the wavelet time-scales or 'periods' when the time scale is not the unit, by the default is 1. - `FLAG`: This 'flag' is used to plot the Y axis of the multivariate time series if the number of these series is less than four, by default is TRUE. - `FLAGNA`: This is used to plot (by the default is 1) or not (please us 0) the correlation coefficients that are/not statistically significant. - `COLS`: The colors used to plot the multivariate time series. - `LTY`: The type of lines used to plot the multivariate time series. - `LWD`: The tick sizes used to plot the multivariate time series. - `DIST`: This parameter is used to define the distances between the Y axis when the multivariate time series are plotted. ## Details The `plot_estim_WLMC` function plots as a heat map the output of the `estim_WLMC` function. This function plot the multivariate time series under analysis and the aforementioned heat map. Another feature of `plot_estim_WLMC` is that this function plot the correlation coefficients that are/not statistically significant, which use blanks to plot the coefficients that are not significant. ## Returns Outputs: A plot of the time series under analysis and a heat map (a multi-plot via screen) of the wavelet 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](https://scholar.google.es/citations?user=8djLIhcAAAAJ&hl=en). Web2: [https://www.researchgate.net/profile/Josue-Polanco-Martinez](https://www.researchgate.net/profile/Josue-Polanco-Martinez). Email: josue.m.polanco@gmail.com Acknowledgement: We acknowledge to the Excellence Unit GECOS (reference number CLU-2019-03), Universidad de Salamanca for its funding support. ## References Fernández-Macho, J. (2018). Time-localized wavelet multiple regression and correlation. Physica A: Statistical Mechanics and its Applications, 492, 1226-1238. . Polanco-Martínez, J. M., Fernández-Macho, J., & Medina-Elizalde, M. (2020). Dynamic wavelet correlation analysis for multivariate climate time series. Scientific Reports, 10(1), 1-11. . Whitcher, B., Guttorp, P., & Percival, D. B. (2000). Wavelet analysis of covariance with application to atmospheric time series. Journal of Geophysical Research: Atmospheres, 105(D11), 14941-14962. . ## Examples ```r # We reproduce Figure 3 left presented in Polanco-Martínez et al. (2020). inputdata <- rdata_climate N <- nrow(inputdata) wf <- "la8" window <- "gaussian" J <- 7 M <- 168 LISTvals <- estim_WLMC(inputdata, wf=wf, J=J, window=window, M=M, Ymaxr=NULL) plot_estim_WLMC(inputdata, LISTvals=LISTvals, J=J, fac=1, FLAG=TRUE, FLAGNA=1, COLS=c("blue", "green", "red"), LTY=c(rep(1,5)), LWD=c(rep(1.2,5)), DIST=c(seq(0, 10, 2.75))) ```

man_plot_estim_WLMC function