PostProcessChain: Computation for maps of posterior probability of population...
In Geneland: Detection of Structure from Multilocus Genetic Data

Computes posterior probabilities of population membership for each pixel of the spatial domain.

1 2	PostProcessChain(coordinates, path.mcmc,nxdom, nydom,burnin)

`coordinates`	Spatial coordinates of individuals. A matrix with 2 columns and one line per individual.
`path.mcmc`	Path to output files directory
`nxdom`	Number of pixel for discretization of the spatial domain in the horizontal direction
`nydom`	Number of pixel for discretization of the spatial domain in the vertical direction
`burnin`	Number of iterations of the chain to throw away. WARNING : this argument should be given the number of stored iterations (and not the number of computed iterations which differ if `thinning` !=1). If you have `nit`=100000 and `thinning`=100, then only 1000 iterations are stored. Then `burnin`=10 will throw away 10 stored iterations, namely 100*10 computed iterations.

`Posterior probability of population membership for each pixel:`	They are written in an ascii file called ‘proba.pop.membership.txt’. Two first columns are coordinates of pixels then one column per population (thus `npopmax` values are computed for each pixel). Images in each column of ‘proba.pop.membership.txt’ are stored starting from the bottom left pixel. First line of ‘proba.pop.membership.txt’ = bottom left pixel , second line of ‘proba.pop.membership.txt’ = upward neighboor of the previous pixel, etc...
`Posterior probability of population membership for each individual:`	They are written in a file named ‘proba.pop.membership.indiv.txt’. One line per individuals and `2+npopmax` columns per individual. Two first columns are spatial coordinates.
`Label of modal population for pixels and individuals:`	They are written in files named ‘modal.pop.txt’ and ‘modal.pop.indiv.txt’ respectively. See the example section of function `MCMC` to see how they can be added in a plot.