spMC (version 0.3.6)

sim_mcs: Multinomial Categorical Simulation

Description

The function simulates a random field through the Multinomial Categorical Simulation technique (MCS).

Usage

sim_mcs(x, data, coords, grid, knn = NULL)

Arguments

x

an object of the class multi_tpfit, typically with the output of the function multi_tpfit.

data

a categorical data vector of length $n$.

coords

an $n x d$ matrix where each row denotes the $d$-D coordinates of data locations.

grid

an $m x d$ matrix where each row denotes the $d$-D coordinates in the simulation grid.

knn

an integer value which specifies the number of k-nearest neighbours for each simulation point. If NULL (by default), all observations are considered.

Value

Details

This method computes an approximation of posterior probabilities $$\Pr\left(Z(\mathbf{s}_0) = z_k \left\vert \bigcap_{i = 1}^n Z(\mathbf{s}_i) = z(\mathbf{s}_i)\right.\right).$$ $where i = 1, ..., n.$ The algorithm is based on the Bayesian maximum entropy approach and it honours both the model structure and observed data.

References

Allard, D., D'Or, D., Froidevaux, R. (2011) An efficient maximum entropy approach for categorical variable prediction. European Journal of Soil Science, 62(3), 381-393.

Sartore, L. (2010) Geostatistical models for 3-D data. M.Phil. thesis, Ca' Foscari University of Venice.

See Also

sim_ck, sim_ik, sim_path