Description Usage Arguments Details Value Examples
View source: R/oneill_leibovici_entropy.R
Compute Parresol and Edwards' entropy, following Parresol and Edwards (2014),
starting from data. References can be found at SpatEntropy
.
1 
data 
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 nonNA 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  #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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