pemt {spMC} | R Documentation |
Multi-directional Transiograms Estimation
Description
The function computes the multi-directional transiograms without any ellipsoidal interpolation for -D sections.
Usage
pemt(data, coords, mpoints, which.dire, max.dist,
tolerance = pi/8, rotation = NULL, mle = "avg")
Arguments
data |
a categorical data vector of length |
coords |
an |
mpoints |
the number of points per axes. It controls the accuracy of images to plot. |
which.dire |
a vector with two chosen axial directions. If omitted, all |
max.dist |
a scalar or a vector of maximum length for the chosen axial directions. |
tolerance |
a numerical value for the tolerance angle (in radians). It's |
rotation |
a numerical vector of length |
mle |
a character value to pass to the function |
Details
A multidimensional transiogram is a diagram which shows the transition probabilities for a single pair of categories. The probability is computed for any lag vector through
where entries of are not ellipsoidally interpolated, but they are estimated for the direction specified by the vector
.
In particular cases, some entries of the estimated matrix might be not finite, so that the exponential matrix is computable and the resulting transition probabilities are set to be
NaN
. If mle = "mlk"
, this problem may be partially solved.
The exponential matrix is evaluated by the scaling and squaring algorithm.
Value
An object of class pemt
is returned.
Author(s)
Luca Sartore drwolf85@gmail.com
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.
Higham, N. J. (2008) Functions of Matrices: Theory and Computation. Society for Industrial and Applied Mathematics.
Sartore, L. (2010) Geostatistical models for 3-D data. M.Phil. thesis, Ca' Foscari University of Venice.
See Also
multi_tpfit_ml
, tpfit_ml
, image.pemt
, plot.transiogram
Examples
data(ACM)
# Compute a 2-D section of a
# multi-directional transiogram
pemt(ACM$MAT3, ACM[, 1:3], 2,
max.dist = c(200, 200, 20),
which.dire=c(1, 3), mle = "mdn")