# plot: Plot SSA object In Rssa: A Collection of Methods for Singular Spectrum Analysis

## Description

This function plots various sorts of figures related to the SSA method.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```## S3 method for class 'ssa' plot(x, type = c("values", "vectors", "paired", "series", "wcor"), ..., vectors = c("eigen", "factor"), plot.contrib = TRUE, numvalues = nsigma(x), numvectors = min(nsigma(x), 10), idx = 1:numvectors, idy, groups) ```

## Arguments

 `x` SSA object holding the decomposition `type` Type of the plot (see 'Details' for more information) `...` Arguments to be passed to methods, such as graphical parameters `vectors` For type = 'vectors', choose the vectors to plot `plot.contrib` logical. If 'TRUE' (the default), the contribution of the component to the total variance is plotted. For ‘ossa’ class, Frobenius orthogonality checking of elementary matrices is performed. If not all matrices are orthogonal, corresponding warning is risen `numvalues` Number of eigenvalues to plot (for type = 'values') `numvectors` Total number of eigenvectors to plot (for type = 'vectors') `idx` Indices of eigenvectors to plot (for type = 'vectors') `idy` Second set of indices of eigenvectors to plot (for type = 'paired') `groups` Grouping used for the decomposition (see `reconstruct`)

## Details

This function is the single entry to various plots of SSA objects. Right now this includes:

values

plot the graph of the component norms.

vectors

plot the eigenvectors.

paired

plot the pairs of eigenvectors (useful for the detection of periodic components).

series

plot the reconstructed series.

wcor

plot the W-correlation matrix for the reconstructed objects.

Additional (non-standard) graphical parameters which can be transfered via ...:

plot.type

lattice plot type. This argument will be transfered as `type` argument to function `panel.xyplot`.

ref

logical. Whether to plot zero-level lines in series-plot, eigenvectors-plot and paired-plot. Zero-level isolines will be plotted for 2d-eigenvectors-plot.

symmetric

logical. Whether to use symmetric scales in series-plot, eigenvectors-plot and paired-plot.

useRaster

logical. For 2d-eigenvector-plot and wcor-plot, indicating whether raster representations should be used. 'TRUE' by default.

col

color vector for colorscale (for 2d- and wcor-plots), given by two or more colors, the first color corresponds to the minimal value, while the last one corresponds to the maximal value (will be interpolated by `colorRamp`)

zlim

for 2d-plot, range of displayed values

at

for 2d-eigenvectors-plot, a numeric vector giving breakpoints along the range of `z`, a list of such vectors or a character string. If a list is given, corresponding list element (with recycling) will be used for each plot panel. For character strings, values 'free' and 'same' are allowed: 'free' means special breakpoints' vectors (will be evaluated automatically, see description of `cuts` argument in 'Details') for each component. 'same' means one breakpoints' vector for all component (will be evaluated automatically too)

cuts

for 2d-reconstruction-plot, the number of levels the range of `z` would be divided into.

fill.color

color or 'NULL'. Defines background color for shaped 2d-eigenvectors plot. If 'NULL', standard white background will be used.

`ssa-object`, `ssa` `plot.reconstruction`,
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46``` ```# Decompose 'co2' series with default parameters s <- ssa(co2) # Plot the eigenvalues plot(s, type = "values") # Plot W-cor matrix for first 10 reconstructed components plot(s, type = "wcor", groups = 1:10) # Plot the paired plot for first 6 eigenvectors plot(s, type = "paired", idx = 1:6) # Plot eigenvectors for first 6 components plot(s, type = "vectors", idx = 1:6) # Plot the first 4 reconstructed components plot(s, type = "series", groups = list(1:4)) # Plot the eigenvalues by points only plot(s, type = "values", plot.type = "p") # Artificial image for 2dSSA mx <- outer(1:50, 1:50, function(i, j) sin(2*pi * i/17) * cos(2*pi * j/7) + exp(i/25 - j/20)) + rnorm(50^2, sd = 0.1) # Decompose 'mx' with default parameters s <- ssa(mx, kind = "2d-ssa") # Plot the eigenvalues plot(s, type = "values") # Plot eigenvectors for first 6 components plot(s, type = "vectors", idx = 1:6, ref = TRUE, at = "same", cuts = 50, plot.contrib = TRUE, symmetric = TRUE) # Plot factor vectors for first 6 components plot(s, type = "vectors", vectors = "factor", idx = 1:6, ref = TRUE, at = "same", cuts = 50, plot.contrib = TRUE, symmetric = TRUE) # Plot wcor for first 12 components plot(s, type = "wcor", groups = 1:12, grid = c(2, 6)) # 3D-SSA example (2D-MSSA) data(Barbara) ss <- ssa(Barbara, L = c(50, 50, 1)) plot(ss, type = "values") plot(ss, type = "vectors", idx = 1:12, slice = list(k = 1), cuts = 50, plot.contrib = TRUE) plot(ss, type = "vectors", idx = 1:12, slice = list(k = 1, i = 1)) plot(ss, type = "vectors", vectors = "factor", idx = 1:12, slice = list(k = 3), cuts = 50, plot.contrib = FALSE) plot(ss, type = "series", groups = 1:12, slice = list(k = 1)) plot(ss, type = "series", groups = 1:12, slice = list(k = 1, i = 1)) plot(ss, plot.method = "xyplot", type = "series", groups = 1:12, slice = list(k = 1, i = 1)) ```