Rssa (version 0.13)

plot.reconstruction: Plot the results of SSA reconstruction

Description

Plot the result of SSA Reconstruction step

Usage

## S3 method for class '1d.ssa.reconstruction':
plot(x, \dots,
     type = c("raw", "cumsum"),
     plot.method = c("native", "matplot", "xyplot"),
     base.series = NULL,
     add.original = TRUE,
     add.residuals = TRUE)
## S3 method for class 'toeplitz.ssa.reconstruction':
plot(x, \dots,
     type = c("raw", "cumsum"),
     plot.method = c("native", "matplot", "xyplot"),
     base.series = NULL,
     add.original = TRUE,
     add.residuals = TRUE)
## S3 method for class 'mssa.reconstruction':
plot(x,
     slice = list(),
     ...,
     type = c("raw", "cumsum"),
     plot.method = c("native", "matplot", "xyplot"),
     na.pad = c("left", "right"),
     base.series = NULL,
     add.original = TRUE,
     add.residuals = TRUE)
## S3 method for class '2d.ssa.reconstruction':
plot(x, \dots,
     type = c("raw", "cumsum"),
     base.series = NULL,
     add.original = TRUE,
     add.residuals = TRUE,
     add.ranges,
     col = grey(c(0, 1)),
     zlim,
     at)
## S3 method for class 'nd.ssa.reconstruction':
plot(x, slice, \dots)

Arguments

x

SSA object holding the decomposition

slice

for `mssa': list with elements named 'series' and 'components'; for `nd.ssa': list with elements named 'i', 'j', 'k' or 'x', 'y', 'z', 't' or 'd1', 'd2', ... or `1`, `2`, ...; works like '['-operator, allows one to select which components from

type

Type of the plot (see 'Details' for more information)

...

Arguments to be passed to methods, such as graphical parameters

plot.method

Plotting method to use: either ordinary all-in-one via matplot or xyplot, or native plotting method of the input time series

na.pad

select how to pad the series of unequal length with NA's

base.series

another SSA reconstruction object, the series of which should be considered as an original. Useful for plotting the results of sequential SSA

add.original

logical, if 'TRUE' then the original series are added to the plot

add.residuals

logical, if 'TRUE' then the residuals are added to the plot

col

color vector for colorscale, 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

add.ranges

logical, if 'TRUE', the range of the components values will be printed in panels captions

Details

Additional (non-standard) graphical parameters applicable to 2D SSA plots can be transfered via ...: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Examples

# Decompose 'co2' series with default parameters
s <- ssa(co2)
r <- reconstruct(s, groups = list(c(1, 4), c(2, 3), c(5, 6)))
# Plot full 'co2' reconstruction into trend, periodic components and noise
plot(r)

# 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")
# Reconstruct
r <- reconstruct(s, groups = list(1, 2:5))
# Plot components, original image and residuals
plot(r)
# Plot cumulative sum of components only
plot(r, type = "cumsum", add.residuals = FALSE, add.original = FALSE)

# Real example: Mars photo
data(Mars)
# Decompose only Mars image (without backgroud)
s <- ssa(Mars, mask = Mars != 0, wmask = circle(50), kind = "2d-ssa")
# Reconstruct and plot trend
plot(reconstruct(s, 1), fill.uncovered = "original")
# Reconstruct and plot texture pattern
plot(reconstruct(s, groups = list(c(13, 14, 17, 18))))

# Decompose 'EuStockMarkets' series with default parameters
s <- ssa(EuStockMarkets, kind = "mssa")
r <- reconstruct(s, groups = list(Trend = 1:2))
# Plot original series, trend and residuals superimposed
plot(r, plot.method = "xyplot", superpose = TRUE,
     auto.key = list(columns = 3),
     col = c("blue", "green", "red", "violet"),
     lty = c(rep(1, 4), rep(2, 4), rep(3, 4)))
# Plot the series separately
plot(r, plot.method = "xyplot", add.residuals = FALSE,
     screens = list(colnames(EuStockMarkets)),
     col = c("blue", "green", "red", "violet"),
     lty = c(rep(1, 4), rep(2, 4), rep(3, 4)))

# 3D-SSA example (2D-MSSA)
data(Barbara)
ss <- ssa(Barbara, L = c(50, 50, 1))
plot(reconstruct(ss, groups = 1), slice = list(k = 1))