predict.multi_tpfit: Compute Theoretical Multidimensional Transiograms

In spMC: Continuous-Lag Spatial Markov Chains

Compute Theoretical Multidimensional Transiograms

Description

The function computes theoretical transition probabilities of a d-D continuous-lag spatial Markov chain for a specified set of lags.

Usage

## S3 method for class 'multi_tpfit'
predict(object, lags, byrow = TRUE, ...)

Arguments

object	an object of the class multi_tpfit, typically with the output of the function multi_tpfit.
lags	a lag vector or matrix of d-D lags.
byrow	a logical value; if TRUE (by default), each row of matrix argument lags will be considered as a lag vector.
...	further arguments passed from other methods.

Details

A d-D continuous-lag spatial Markov chain is probabilistic model which is developed by interpolation of the transition rate matrices computed for the main directions. 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} (\Vert h \Vert R),

where h is the lag vector and the entries of R are ellipsoidally interpolated.

Value

An object of the class multi_transiogram is returned. The print.multi_transiogram function is used to print computed probabilities. The object is a list with the following components:

Tmat	a 3-D array containing the probabilities.
lags	a matrix containing the lag vectors.
type	a character string which specifies that computed probabilities are theoretical.

Author(s)

Luca Sartore [e-mail]

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.

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

See Also

multi_tpfit, print.multi_tpfit, image.multi_tpfit, tpfit, transiogram

Examples

data(ACM)

# Estimate the parameters of a 
# multidimensional MC model
RTm <- multi_tpfit(ACM$MAT3, ACM[, 1:3])

# Generate the matrix of 
# multidimensional lags
lags <- expand.grid(X=-1:1, Y=-1:1, Z=-1:1)
lags <- as.matrix(lags)

# Compute transition probabilities 
# from the multidimensional MC model
predict(RTm, lags)

spMC documentation built on May 3, 2023, 9:13 a.m.