plot_wave.multiple.cross.regression: Auxiliary routine for plotting wave multiple...
In jfdezmacho/wavemulcor: Wavelet Routines for Global and Local Multiple Regression and Correlation
Description
Usage
Arguments
Details
Value
Author(s)
References
Description
Produces a plot of wave multiple cross.regressions.
Usage
1
plot_wave.multiple.cross.regression(Lst, lmax, nsig=2, by=3)
Arguments
Lst
A list from wave.multiple.cross.regression.
lmax
maximum lag (and lead).
nsig
An optional value for the number of significant variables to plot_ Default is 2.
by
labels are printed every lmax/by. Default is 3.
Details
The routine produces a plot of wave multiple regressions, one per wavelet level,
with their confidence intervals.
Also, the name of the variable that maximizes that multiple correlation against the rest is shown on top.
The others are named with their order of significance when they are relevant.
Value
Plot.
Author(s)
Javier Fernández-Macho,
Dpt. of Quantitative Methods,
University of the Basque Country, Agirre Lehendakari etorb. 83, E48015 BILBAO, Spain.
(email: javier.fernandezmacho at ehu.eus).
References
Fernández-Macho, J., 2018. Time-localized wavelet multiple regression and correlation, Physica A:
Statistical Mechanics, vol. 490, p. 1226–1238. <DOI:10.1016/j.physa.2017.11.050>
jfdezmacho/wavemulcor documentation built on Sept. 8, 2021, 1:51 a.m.
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Description Usage Arguments Details Value Author(s) References
Description
Produces a plot of wave multiple cross.regressions.
Usage
1 | plot_wave.multiple.cross.regression(Lst, lmax, nsig=2, by=3)
|
Arguments
Lst |
A list from wave.multiple.cross.regression. |
lmax |
maximum lag (and lead). |
nsig |
An optional value for the number of significant variables to plot_ Default is 2. |
by |
labels are printed every lmax/by. Default is 3. |
Details
The routine produces a plot of wave multiple regressions, one per wavelet level, with their confidence intervals. Also, the name of the variable that maximizes that multiple correlation against the rest is shown on top. The others are named with their order of significance when they are relevant.
Value
Plot.
Author(s)
Javier Fernández-Macho, Dpt. of Quantitative Methods, University of the Basque Country, Agirre Lehendakari etorb. 83, E48015 BILBAO, Spain. (email: javier.fernandezmacho at ehu.eus).
References
Fernández-Macho, J., 2018. Time-localized wavelet multiple regression and correlation, Physica A: Statistical Mechanics, vol. 490, p. 1226–1238. <DOI:10.1016/j.physa.2017.11.050>
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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