plot_local.multiple.cross.regression: Auxiliary routine for plotting local 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 local multiple cross-regressions.
Usage
1
plot_local.multiple.cross.regression(Lst, lmax, nsig=2, xaxt="s")
Arguments
Lst
A list from local.multiple.cross.regression.
lmax
maximum lag (and lead).
nsig
An optional value for the number of significant variables to plot_ Default is 2.
xaxt
An optional vector of labels for the "x" axis. Default is 1:n.
Details
The routine produces time series plots of local multiple cross-regressions
with their confidence interval for every lag and lead.
Also, at every upturn and downturn of the corresponding local multiple cross-correlation,
the name of the variable that maximizes that multiple correlation against the rest is shown on top.
The others are named ordered by 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 local multiple cross-regressions.
Usage
1 | plot_local.multiple.cross.regression(Lst, lmax, nsig=2, xaxt="s")
|
Arguments
Lst |
A list from local.multiple.cross.regression. |
lmax |
maximum lag (and lead). |
nsig |
An optional value for the number of significant variables to plot_ Default is 2. |
xaxt |
An optional vector of labels for the "x" axis. Default is 1:n. |
Details
The routine produces time series plots of local multiple cross-regressions with their confidence interval for every lag and lead. Also, at every upturn and downturn of the corresponding local multiple cross-correlation, the name of the variable that maximizes that multiple correlation against the rest is shown on top. The others are named ordered by 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
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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