# plot

##### Plot SSA object

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

##### Usage

```
# S3 method for 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.

##### See Also

##### Examples

```
# NOT RUN {
# 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))
# }
```

*Documentation reproduced from package Rssa, version 1.0, License: GPL (>= 2)*