Plot the revisability of an object of class "prova_mi" (mutual information) as a histogram

The mutual information calculated with the mutualinfo() function, and outputted as a "prova_mi" (mutual information) 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 "prova_mi" (mutual information) 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 such change would be. The distribution is represented by a histogram formed from samples of revised mutual information. The bin size is chosen according to the Monte Carlo accuracy.

Usage

# S3 method for class 'prova_mi'
hist(
  x,
  breaks = NULL,
  lty = c(1, 2, 4, 3, 6, 5),
  lwd = 2,
  col = palette(),
  alpha.f = 1,
  alpha.f.fill = 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 "prova_mi" (mutual information), obtained with mutualinfo().

breaks

as in function graphics::hist(), or NULL (default). Value NULL determines the bin width from the Monte Carlo accuracy (roughly speaking, each bin spans two standard deviations).

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

see analogous arguments in graphics::matplot()

alpha.f.fill

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

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

Examples

## Use the "prova_K" (knowledge) object 'Kexample',
## calculated from the "penguins" dataset;
## variates: 'species' and 'bill_len'

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

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