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

1 2 3 |

`x` |
Object of class tbss. |

`first` |
Number of components with maximal kurtosis to be selected. See |

`last` |
Number of components with minimal kurtosis to be selected. See |

`main` |
The title of the plot. |

`datatype` |
Parameter for choosing the type of plot, either |

`...` |
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

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 | ```
library(ElemStatLearn)
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)
library("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)
``` |

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