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.
tpfit_me(data, coords, direction, tolerance = pi/8,
max.it = 9000, mle = "avg")
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:
the transition rates matrix computed for the user defined direction.
a vector containing the proportions of each observed category.
a numerical value which denotes the tolerance angle (in radians).
a categorical data vector of length
an
a
a numerical value for the tolerance angle (in radians). It is pi/8
by default.
a numerical value which denotes the maximum number of iterations to perform during the optimization phase. It is 9000
by default.
a character value to pass to the function mlen
. It is "avg"
by default.
Luca Sartore drwolf85@gmail.com
A 1-D continuous-lag spatial Markov chain is probabilistic model which involves a transition rate matrix
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
When some entries of the matrix tolerance
coefficient or to set the input argument mle
to "mlk"
.
Carle, S. F., Fogg, G. E. (1997) Modelling Spatial Variability with One and Multidimensional Continuous-Lag Markov Chains. Mathematical Geology, 29(7), 891-918.
predict.tpfit
, print.tpfit
, multi_tpfit_me
# \donttest{
data(ACM)
# Estimate the parameters of a
# one-dimensional MC model
tpfit_me(ACM$MAT5, ACM[, 1:3], c(0,0,1))
# }
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