WMC: Wavelet multiple correlation (multivariate case).

In W2CWM2C: A Graphical Tool for Wavelet (Cross) Correlation and Wavelet Multiple (Cross) Correlation Analysis

Description

The WMC function generates a plot to the wavelet routine for multiple correlation
(wave.multiple.correlation) from the wavemulcor package (Fernandez-Macho 2012b). The WMC plot output can be displayed in the screen (by default) or can be saved as PNG, JPG, PDF or EPS. Furthermore, it also provides a way to handle multivariate time series easily as a list of N elements (time series).

Usage

WMC(inputDATA, Wname, J, device="screen", filename,
          Hfig, WFig, Hpdf, Wpdf)

Arguments

inputDATA	A couple of 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.
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'.

Details

The WMC function helps to make and save easily the plot of the multiple correlation routine
(wave.multiple.correlation) of the wavemulcor package (Fernandez-Macho 2012b). The WMC function also helps to manage easily multivariate time series to use the Wavelet multiple correlation routine.

Value

Output:

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

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

Note

Needs wavemulcor (to compute the wave.multiple.correlation) and waveslim packages (to compute the modwt and the brick.wall).

Author(s)

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: [e-mail].

References

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>.

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.

Examples

 # This example is the wavelet multiple correlation (WMC) version of 
 # the Figure 7 in Polanco-Martinez and Fernandez-Macho (2014).

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

 #:: Transform to 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!
 dataexample  <- dataexample[,1:5]
 lrdatex      <- apply(log(dataexample), 2, diff)
 inputDATA    <- ts(lrdatex, start=1, frequency=1)

 #Input parameters 
  Wname       <- "la8"
  J           <- 8
  compWMC     <- WMC(inputDATA, Wname, J, device="screen", NULL,
                     NULL, NULL, NULL, NULL)

W2CWM2C documentation built on Jan. 13, 2021, 11:54 a.m.