spMC (version 0.2.2)

metpfit: Maximum Entropy Method for One-dimensional Model Parameters Estimation

Description

The function estimates the model parameters of a 1-D continuous lag spatial Markov chain by the use of the maximum entropy method. Transition rates matrix along a user defined direction and proportions of categories are computed.

Usage

metpfit(data, coords, direction, tolerance = pi/8,
        max.it = 9000, mle = FALSE)

Value

An object of the class tpfit is returned. The function print.tpfit is used to print the fitted model. The object is a list with the following components:coefficientsthe transition rates matrix computed for the user defined direction.propa vector containing the proportions of each observed category.tolerancea numerical value which denotes the tolerance angle (in radians).

Rdversion

1.1

Details

A 1-D continuous-lag spatial Markov chain is probabilistic model which involves a transition rate matrix $R$ computed for the direction $\phi$. It defines the transition probability $\Pr(Z(s + h) = z_k | Z(s) = z_j)$ through the entry $t_{jk}$ of the following matrix $$T = \mbox{expm} (h R),$$ where $h$ is a positive lag value.

To calculate entries of the transition rate matrix, we need to maximize the entropy of the transition probabilities of embedded occurrences along a given direction $\phi$. The entropy is defined as $$e = - \sum_{k}^K \sum_{j \neq k}^K \tau_{jk, \phi} \log \tau_{jk, \phi},$$ where $\tau_{jk, \phi}$ are transition probabilities of embedded occurrences. It is maximized by the use of the iterative proportion fitting method.

When some entries of the matrix $R$ are not identifiable, it is suggested to vary the tolerance coefficient or to set to TRUE the input argument mle.

References

Carle, S. F., Fogg, G. E. (1997) Modelling Spatial Variability with One and Multidimensional Continuous-Lag Markov Chains. Mathematical Geology, 29(7), 891-918.

See Also

predict.tpfit, print.tpfit, multi.metpfit

Examples

data(ACM)

# Estimate the parameters of a 
# one-dimensional MC model
metpfit(ACM$MAT5, ACM[, 1:3], c(0,0,1), 100)