plot.iva {ivaBSS}

Plotting an Object of Class iva

Description

plot method for the class "iva".

Usage

## S3 method for class 'iva'
plot(x, which.dataset = NA, which.source = NA,
type = "l", xlabs = c(), ylabs = c(), colors = c(),
oma = c(1, 1, 0, 0), mar = c(2, 2, 1, 1), ...)

Arguments

x

An object of class "iva", usually the result of a call to NewtonIVA or fastIVA.

which.dataset

Positive integer to determine which dataset is returned. If not set, returns all datasets.

which.source

Positive integer to determine which dataset is returned. If not set, returns all datasets.

type

1-character string giving the type of plot desired. For details, see plot.

xlabs

Vector containing the labels for x-axis.

ylabs

Vector containing the labels for y-axis.

colors

Vector containing the colors for each plot.

oma

A vector of the form c(bottom, left, top, right) giving the size of the outer margins in lines of text. For more details, see par.

mar

A numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot. For more details, see par.

...

Further arguments passed to plot function.

Details

Plots either all estimated sources of the object of class "iva" or the estimates for specific dataset and/or source.

Value

No return value, called for plotting the estimated sources of the object of class "iva".

Author(s)

Mika Sipilä

See Also

NewtonIVA, fastIVA

Examples

if (require("LaplacesDemon")) {
  # Generate sources from multivariate Laplace distribution
  P <- 4; N <- 1000; D <- 4;
  S <- array(NA, c(P, N, D))

  for (i in 1:P) {
    U <- array(rnorm(D * D), c(D, D))
    Sigma <- crossprod(U)
    S[i, , ] <- rmvl(N, rep(0, D), Sigma)
  }

  # Generate mixing matrices from standard normal distribution
  A <- array(rnorm(P * P * D), c(P, P, D))

  # Generate mixtures
  X <- array(NaN, c(P, N, D))
  for (d in 1:D) {
    X[, , d] <- A[, , d] %*% S[, , d]
  }

  # Estimate sources and unmixing matrices
  res_G <- NewtonIVA(X, source_density = "gaussian")

  # Plot all estimated sources
  plot(res_G)

  # Plot the source estimates for the first dataset only
  plot(res_G, which.dataset = 1)

  # Plot the source estimates for the second source only
  plot(res_G, which.source = 2)

  # Plot the source estimate of the second dataset and third source
  plot(res_G, which.dataset = 2, which.source = 3, type = "p")

  # Plot all source estimates with custom colors and labels
  plot(res_G, col=c(rep(1, 4), rep(2, 4), rep(3, 4), rep(4, 4)),
      xlabs = c("Subject 1", "Subject 2", "Subject 3", "Subject 4"),
      ylabs = c("Channel 1", "Channel 2", "Channel 3", "Channel 4"))
}