plot.mvLSW | R Documentation |
Plot the data contained within a mvLSW
object based on the
requested format.
## S3 method for class 'mvLSW' plot(x, style = 1, info = NULL, Interval = NULL, diag = TRUE, sub = "Spectrum", ...)
x |
A |
style |
Index stating the type of plotting format for
the |
info |
Vector containing the channel and/or level indices
defining the slice through |
Interval |
A list containing two items, both |
diag |
Logical, should the diagonal panels be drawn when
|
sub |
Plot subtitle. Set to |
... |
Additional graphical parameters. |
This command plots the data contained within the mvLSW
based
on requested plotting style.
Plotting style style=1
with information info=c(p,q,j)
generates a single plot for a
specified channel pair p
& q
and level j
.
Plotting style style=2
with information info=j
creates a set of plots from x
for all channel pairs in a
lower-triangular panel corresponding to the specified level j.
If diag=FALSE
then the plots along the diagonal are suppressed,
which is ideal when x
contain coherence estimates.
Plotting style style=3
with information info=c(p,q)
creates a set of plots from x
for all levels (from fine
to coarse) for channel pair p
and q
.
Finally, the plotting style style=4
with information
info=c(p,q)
presents the same information as
for the previous case, but in a compact matrix format. Please
refer to image.plot
from the fields
library for
additional information on this plotting style.
The argument Interval
must be supplied in order to draw a
polygon depicting the pointwise interval. See ApxCI
for deriving an approximate confidence interval for the evolutionary
wavelet spectrum estimate.
This argument is ignored in the case style=4
.
Generates a plot. No data is returned.
Taylor, S.A.C., Park, T.A. and Eckley, I. (2019) Multivariate locally stationary wavelet analysis with the mvLSW R package. Journal of statistical software 90(11) pp. 1–16, doi: 10.18637/jss.v090.i11.
plot.default
, image.plot
, as.mvLSW
,
mvEWS
, coherence
, ApxCI
.
## Define evolutionary wavelet spectrum, structure only on level 2 Spec <- array(0, dim=c(3, 3, 8, 256)) Spec[1, 1, 2, ] <- 10 Spec[2, 2, 2, ] <- c(rep(5, 64), rep(0.6, 64), rep(5, 128)) Spec[3, 3, 2, ] <- c(rep(2, 128), rep(8, 128)) Spec[2, 1, 2, ] <- Spec[1, 2, 2, ] <- punif(1:256, 65, 192) Spec[3, 1, 2, ] <- Spec[1, 3, 2, ] <- c(rep(-1, 128), rep(5, 128)) Spec[3, 2, 2, ] <- Spec[2, 3, 2, ] <- -0.5 EWS <- as.mvLSW(x = Spec, filter.number = 1, family = "DaubExPhase", min.eig.val = NA) ## Sample time series and estimate the EWS and coherence. set.seed(10) X <- rmvLSW(Spectrum = EWS) EWS_X <- mvEWS(X, kernel.name = "daniell", kernel.param = 20) RHO_X <- coherence(EWS_X, partial = FALSE) ## Evaluate asymptotic spectral variance SpecVar <- varEWS(EWS_X) ## Evaluate 95% approximate confidence interval CI <- ApxCI(object = EWS_X, var = SpecVar, alpha=0.05) ## Plot mvEWS between channels 1 & 3 at level 2 plot(x = EWS_X, style = 1, info = c(1, 3, 2), Interval = CI) ## Plot coherence between channels 1 & 3 at level 2 plot(x = RHO_X, style = 1, info = c(1, 3, 2), ylab = "Coherence") ## mvEWS panel plot for level 2 plot(x = EWS_X, style = 2, info = 2, Interval = CI) ## Panel plot of coherence for level 2 plot(x = RHO_X, style = 2, info = 2, diag = FALSE, ylab = "Coherence") ## Plot mvEWS for channel pair 1 & 3 at all levels plot(x = EWS_X, style = 3, info = c(1, 3), Interval = CI) ## Plot coherence for channel pair 1 & 3 at all levels plot(x = RHO_X, style = 3, info = c(1, 3), ylab = "Coherence") ## Image plot for coherence between channels 1 & 3 plot(x = RHO_X, style = 4, info = c(1, 3), sub = "Coherence")
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