The function estimates the model parameters of a 1-D continuous lag spatial Markov chain by the use of the mean length method. Transition rates matrix along a user defined direction and proportions of categories are computed.
tpfit_ml(data, coords, direction, tolerance = pi/8, 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 \(n\).
an \(n \times d\) matrix where each row denotes the \(d\)-D coordinates of data locations.
a \(d\)-D numerical vector (or versor) which represents the chosen direction.
a numerical value for the tolerance angle (in radians). It's pi/8 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 \(R\) 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 compute the mean lengths and the embedded transition probabilities.
By the use of the mean lengths, diagonal entries of \(R\) are computed as $$\hat{r}_{kk} = \frac{1}{\bar{L}_k},$$ where \(\bar{L}_k\) is the mean length of the \(k\)-th category.
The off-diagonal transition rates of the matrix \(R\) are estimated by the use of embedded transition probabilities and mean lengths: $$\hat{r}_{jk} = \frac{\pi_{jk}}{\bar{L}_k}, \quad \forall j \neq k, $$ where \(\pi_{jk}\) is a specific embedded transition probability.
When some entries of the matrix \(R\) are not identifiable, it is suggested to vary the 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.
Sartore, L. (2010) Geostatistical models for 3-D data. M.Phil. thesis, Ca' Foscari University of Venice.
predict.tpfit, print.tpfit, multi_tpfit_ml, transiogram
# \donttest{
data(ACM)
# Estimate the parameters of a
# one-dimensional MC model
tpfit_ml(ACM$MAT5, ACM[, 1:3], c(0, 0, 1))# }
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