WMCC: Wavelet multiple cross-correlation (multivariate case).
In W2CWM2C: A Graphical Tool for Wavelet (Cross) Correlation and Wavelet Multiple (Cross) Correlation Analysis

Description Usage Arguments Details Value Note Author(s) References Examples

View source: R/WMCC.R

The WMCC function (multivariate case) computes the wavelet multiple cross correlation by means of the function wave.multiple.cross.correlation from the wavemulcor package (Fernandez-Macho 2012b) and present the result as a novel plot that reduce the number of plots of the classical function wave.multiple.cross.correlation. The WMCC plot output can be displayed in the screen (by default) or can be saved as PNG, JPG, PDF or EPS. The WMCC function also provides a way to handle multivariate time series easily as a list of N elements (time series).

1 2	WMCC(inputDATA, Wname, J, lmax, device="screen", filename, Hfig, WFig, Hpdf, Wpdf)

`inputDATA`	An array of multivariate time series as a ts object (please, check the ts manual to get more information about the ts function in R).
`Wname`	The wavelet function or filter to use in the decomposition.
`J`	Specifies the depth of the decomposition.
`lmax`	The maximum lag.
`device`	The type of the output device (by default the option is “screen”, and the other options are “jpg”, “png”, “eps” and “pdf”).
`filename`	The output filename.
`Hfig`	The height of the 'jpg' or 'png' image.
`WFig`	The width of the 'jpg' or 'png' image.
`Hpdf`	The height of the eps or pdf.
`Wpdf`	The width of the eps or pdf.

The WMCC function compute the wavelet multiple cross correlation using the function
wave.multiple.cross.correlation from the wavemulcor package (Fernandez-Macho 2012b), but the WMCC function incorporates some graphical improvements (please, look at Figure 7 in Polanco-Martinez and Fernandez-Macho 2014), such as the reduction of the number of plots to present the results of the function wave.multiple.cross.correlation.

Output:
Output plot: screen or 'filename + .png, .jpg, .eps or .pdf'.

Output data: The same list of elements of the function wave.multiple.cross.correlation of the wavemulcor package (Fernandez-Macho 2012b).

Needs wavemulcor (to compute the wave.multiple.cross.correlation) and waveslim packages (to compute the modwt and the brick.wall) and also needs the colorspace package to plot the heatmaps.

Josue M. Polanco-Martinez (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: josue.m.polanco@gmail.com.

Fernandez-Macho, J. (2012a). Wavelet multiple correlation and cross-correlation: A multiscale analysis of euro zone stock markets. Physica A: Statistical Mechanics and its Applications,
391(4):1097–1104. doi: 10.1016/j.physa.2011.11.002.

Fernandez-Macho, J. (2012b). wavemulcor: Wavelet routine for multiple correlation. R package version 1.2, The Comprehensive R Archive Network (CRAN), <URL: https://cran.r-project.org/package=wavemulcor>.

Ihaka, R., Murrell, P., Hornik, K., Fisher, J. C. and Zeileis, A. (2012). colorspace: Color Space Manipulation. R package version 1.2.0, The Comprehensive R Archive Network (CRAN), <URL: https://cran.r-project.org/package=colorspace>.

Polanco-Martinez, J. and J. Fernandez-Macho (2014). The package 'W2CWM2C': description, features and applications. Computing in Science & Engineering, 16(6):68–78. doi: 10.1109/MCSE.2014.96.

 library("colorspace")
 library("wavemulcor")
 library("W2CWM2C")
 data(dataexample)

 #:: Figure 7 (Polanco-Martinez and Fernandez-Macho (2014).

 #:: Transform log returns using: ln(t + deltat) - ln(t). 
 #:: The application in this example uses stock market 
 #:: indexes (it is common to use log returns instead of
 #:: raw data). Other kinds of pre-processing data are possible. 

 dataexample  <- dataexample[-1] #remove the dates!
 lrdatex      <- apply(log(dataexample), 2, diff)
 inputDATA    <- ts(lrdatex, start=1, frequency=1)

 Wname     <- "la8"
 J         <- 8
 lmax      <- 30
 compWCC   <- WMCC(inputDATA, Wname, J, lmax, device="screen", NULL,
                   NULL, NULL, NULL, NULL)