# Plot mvLSW Object Plot the data contained within a `mvLSW` object based on the requested format. methods ```r ## S3 method for class 'mvLSW' plot(x, style = 1, info = NULL, Interval = NULL, diag = TRUE, sub = "Spectrum", ...) ``` ## Arguments - `x`: A `mvLSW` object. - `style`: Index stating the type of plotting format for the `mvLSW` object. (See details.) - `info`: Vector containing the channel and/or level indices defining the slice through `x` according to the requested plotting `style`. (See details.) - `Interval`: A list containing two items, both `mvLSW` objects with names "L" and "U" that respectively define the lower and upper pointwise interval values. If `NULL`, default, then no interval is plotted. - `diag`: Logical, should the diagonal panels be drawn when `style=2`. Ideally this should be `FALSE` if `object` contains the coherence. Set to `TRUE` by default. - `sub`: Plot subtitle. Set to `"Spectrum"` by default. - `...`: Additional graphical parameters. ## Details 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`. ## Returns Generates a plot. No data is returned. ## References 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. ## See Also `plot.default`, `image.plot`, `as.mvLSW`, `mvEWS`, `coherence`, `ApxCI`. ## Examples ```r ## 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") ```

plot.mvLSW function