SpatEntropy (version 2.2-4)

leibovici: Leibovici's entropy.

Description

This function computes Leibovici's entropy according to a chosen distance \(d\) (with O'Neill's entropy as a special case) following Leibovici (2009), see also Altieri et al (2017). References can be found at SpatEntropy.

Usage

leibovici(
  data,
  cell.size = 1,
  ccdist = cell.size[1],
  win = NULL,
  verbose = F,
  plotout = T
)

Value

a list of four elements:

• leib Leibovici's entropy

• range the theoretical range of Leibovici's entropy, from 0 to \(\log(I^2)\)

• rel.leib Leibovici's relative entropy

• probabilities a table with absolute frequencies and estimated probabilities (relative frequencies) for all couple categories

Moreover, a plot of the dataset is produced. Over the plot, a random point is displayed as a red star, and a circle is plotted around that point. The radius of the circle is set by ccdist, so that a visual idea is given about the choice of the distance for building co-occurrences.

Arguments

data

If data are lattice, a data matrix, which can be numeric, factor, character, ... If the dataset is a point pattern, data is a ppp object.

cell.size

A single number or a vector of length two, only needed if data are lattice. It gives the length of the side of each pixel; if the pixel is rectangular, the first number gives the horizontal side and the second number gives the vertical side. Default to 1. Ignored if data are points.

ccdist

A single number. The chosen distance for selecting couples of pixels/points within the observation area. Default to cell.size[1].

win

Optional, an object of class owin, the observation window for data plotting

verbose

Logical. If TRUE an output is printed in order to follow the progress of the work (recommended for large dataset). Default set to FALSE.

plotout

Logical. Default to TRUE, produces an informative plot as part of the function output.

Details

This index is based on the transformed variable \(Z\) identifying couples of realizations of the variable of interest. A distance of interest is fixed, which in the case of O'Neill's entropy is the contiguity, i.e. sharing a border for lattice data. Then, all couples of realizations of the variable of interest lying at a distance smaller or equal to the distance of interest are counted, and their relative frequencies are used to compute the index with the traditional Shannon's formula. #'$$H_L(Z)=\sum p(z_r|d) \log(1/p(z_r|d))$$ where \(z_r=(x_i, x_{i'})\) is a generic couple of realizations of the study variable \(X\). The conditioning on \(d\) means that only couples within a predefined distance are considered. The maximum value for Leibovici's entropy is \(\log(I^2)\) where \(I\) is the number of categories of the study variable \(X\). The relative version of Leibovici's entropy is obtained by dividing the entropy value by its maximum, and is useful for comparison across datasets with a different number of categories. The function is able to work with grids containing missing data, specified as NA values. All NAs are ignored in the computation and only couples of non-NA observations are considered.

Examples

#random grid data - high entropy
data=matrix(sample(c("a","b","c"), 400, replace=TRUE), nrow=20)
leibovici(data, cell.size=1, ccdist=2)
#plot data
plot(as.im(data, W=square(nrow(data))),
     col=grDevices::gray(seq(1,0,length.out=length(unique(c(data))))),
     main="", ribbon=TRUE)

#compact grid data - low entropy
data=matrix(sort(sample(c("a","b","c"), 400, replace=TRUE)), nrow=20)
#Note: with sorted data, only some couple categories will be present
leibovici(data, cell.size=1, ccdist=1.5)
#plot data
plot(as.im(data, W=square(nrow(data))),
     col=heat.colors(length(unique(c(data)))),
     main="", ribbon=TRUE)

#point data
data=ppp(x=runif(400), y=runif(400),
window=square(1), marks=sample(1:4, 400, replace=TRUE))
leibovici(data, ccdist=0.1)
#plot data
plot(data)