plot.tbss: Plot an Object of the Class tbss
In tensorBSS: Blind Source Separation Methods for Tensor-Valued Observations

Description Usage Arguments Details Author(s) Examples

Plots the most interesting components (in the sense of extreme kurtosis) obtained by a tensor blind source separation method.

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## S3 method for class 'tbss'
plot(x, first = 2, last = 2, datatype = NULL, 
     main = "The components with most extreme kurtoses", ...)

`x`	Object of class tbss.
`first`	Number of components with maximal kurtosis to be selected. See `selectComponents` for details.
`last`	Number of components with minimal kurtosis to be selected. See `selectComponents` for details.
`main`	The title of the plot.
`datatype`	Parameter for choosing the type of plot, either `NULL`, `"iid"` or `"ts"`. The default `NULL` means the value from the tbss object `x` is taken.
`...`	Further arguments to be passed to the plotting functions, see details.

The function plot.tbss first selects the most interesting components using selectComponents and then plots them either as a matrix of scatter plots using pairs (datatype = "iid") or as a time series plot using plot.ts (datatype = "ts"). Note that for tSOBI this criterion might not necessarily be meaningful as the method is based on second moments only.

Joni Virta

data(zip.train)
x <- zip.train

rows <- which(x[, 1] == 0 | x[, 1] == 1)
x0 <- x[rows, 2:257]
y0 <- x[rows, 1] + 1

x0 <- t(x0)
dim(x0) <- c(16, 16, 2199)

tfobi <- tFOBI(x0)
plot(tfobi, col=y0)

if(require("stochvol")){
  n <- 1000
  S <- t(cbind(svsim(n, mu = -10, phi = 0.98, sigma = 0.2, nu = Inf)$y,
               svsim(n, mu = -5, phi = -0.98, sigma = 0.2, nu = 10)$y,
               svsim(n, mu = -10, phi = 0.70, sigma = 0.7, nu = Inf)$y,
               svsim(n, mu = -5, phi = -0.70, sigma = 0.7, nu = 10)$y,
               svsim(n, mu = -9, phi = 0.20, sigma = 0.01, nu = Inf)$y,
               svsim(n, mu = -9, phi = -0.20, sigma = 0.01, nu = 10)$y))
  dim(S) <- c(3, 2, n)

  A1 <- matrix(rnorm(9), 3, 3)
  A2 <- matrix(rnorm(4), 2, 2)

  X <- tensorTransform(S, A1, 1)
  X <- tensorTransform(X, A2, 2)

  tgfobi <- tgFOBI(X)
  plot(tgfobi, 1, 1)
}