Plot the revisability of an object of class "mi" as a histogram

The mutual information calculated with the mutualinfo() function, and outputted as a "mi" object, has an associated "revisability" that comes from the finite size of the data sample. A much larger sample might reveal a different value of mutual information.

The hist() method for a "mi" object is a utility to visualize this kind of revisability, in the form of a distribution: it shows how the mutual information could change, if we collected a much larger (infinite) data sample, and how likely would such change be.

Usage

# S3 method for class 'mi'
hist(
  x,
  breaks = NULL,
  lty = c(1, 2, 4, 3, 6, 5),
  lwd = 2,
  col = palette(),
  alpha.f = 1,
  fill.alpha.f = 0.125,
  showvalue = TRUE,
  xlab = NULL,
  ylab = NULL,
  xlim = NULL,
  ylim = c(0, NA),
  main = NULL,
  grid = TRUE,
  axes = FALSE,
  add = FALSE,
  ...
)

Arguments

x

Object of class "mi", obtained with mutualinfo().

breaks

as in function graphics::hist(), or NULL (default). Value NULL uses the geometric mean of Sturges and Freedman-Diaconis bins.

lty, lwd, col, alpha.f, xlab, ylab, xlim, ylim, main, grid, axes, add

see analogous arguments in graphics::matplot()

fill.alpha.f

Numeric, default 0.125: opacity of the histogram filling. 0 means no filling.

showvalue

Logical, default TRUE: show the mutual information obtained from the current data sample?

...

Other parameters to be passed to pplot().

Value

Invisibly, an object of class "histogram".

See also

mutualinfo() to calculate mutual information and its revisability.

print.mi() ] to plot mutual information and quantiles calculated by mutualinfo()

pplot() (on which hist.mi() is based) for more general plots.

Examples

## Load the example `K`nowledge object calculated from the "penguins" dataset;
## variates: 'species' and 'bill_len'
K <- Kexample

## calculate the mutual information and its revisability
MI <- mutualinfo(Y1names = 'species', Y2names = 'bill_len', K = K, nv = 2)

## show the possible revisability of the mutual information,
## if a much larger data sample were collected
hist(MI)