View source: R/image.multi_tpfit.R
image.multi_tpfit | R Documentation |
The function plots 2
-D sections of a predicted multidimensional transiograms computed through ellipsoidal interpolation.
## S3 method for class 'multi_tpfit'
image(x, mpoints, which.dire, max.dist, main,
mar, ask = TRUE, ..., nlevels = 10, contour = TRUE)
x |
an object of the class |
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. |
main |
the main title (on top) whose font and size are fixed. |
mar |
a scalar or a numerical vector of the form |
ask |
a logical value; if |
... |
other arguments to pass to the function |
nlevels |
the number of levels to pass to the function |
contour |
logical. If |
A multidimensional transiogram is a diagram which shows the transition probabilities for a single pair of categories. It is computed for any lag vector h
through
\mbox{expm} (\Vert h \Vert R),
where entries of R
are ellipsoidally interpolated (see multi_tpfit
).
The exponential matrix is evaluated by the scaling and squaring algorithm.
An image is produced on the current graphics device. No values are returned.
Luca Sartore drwolf85@gmail.com
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.
multi_tpfit
, pemt
, image.pemt
, image
, plot.transiogram
data(ACM)
# Estimate model parameter
x <- multi_tpfit(ACM$MAT5, ACM[, 1:3])
# Set short names for categories 3 and 4
names(x$prop)[3:4] <- c("Clay and Sand", "Gravel and Sand")
# Plot 2-D theoretical sections of
# a multidimensional transiogram
image(x, 40, max.dist=c(200,200,20), which.dire=2:3,
mar = .7, col=rev(heat.colors(500)),
breaks=0:500/500, nlevels = 5)
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