tensorBSS (version 0.3.9)

plot.tbss: Plot an Object of the Class tbss

Description

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

Usage

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

Arguments

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.

Author

Joni Virta

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.

Examples

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)
}