Compute Parresol and Edwards' entropy, following Parresol and Edwards (2014),
starting from data. References can be found at
A data matrix or vector, can be numeric, factor, character, ...
This index is based on the transformed variable Z identifying couples of realizations of the variable of interest. A distance of interest is fixed: Parresol and Edwards' entropy is thought for areas sharing a border, as O'Neill's entropy. All contiguous couples of realizations of the variable of interest are counted and their relative frequencies are used to compute the index, which is the opposite of O'Neill's entropy. 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.
a list of two elements:
probabilities - a table with the estimated probabilities (relative frequencies) for all couple categories;
parredw - Parresol and Edwards' entropy.
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#numeric data, square grid data=matrix(sample(1:5, 100, replace=TRUE), nrow=10) parredw(data) #plot data plot(as.im(data, W=square(nrow(data))), col=gray(seq(1,0,l=length(unique(c(data))))), main="", ribbon=TRUE) #character data, rectangular grid data=matrix(sample(c("a","b","c"), 300, replace=TRUE), nrow=30) parredw(data) #plot data plot(as.im(data, W=owin(xrange=c(0,ncol(data)), yrange=c(0,nrow(data)))), col=terrain.colors(length(unique(c(data)))), main="", ribbon=TRUE)
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