Description Usage Arguments Details See Also Examples

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

1 2 3 4 5 6 7 8 9 10 11 |

`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 |

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))
``` |

Rssa documentation built on Oct. 4, 2017, 5:06 p.m.

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