Description Usage Arguments Value Author(s) References See Also Examples
View source: R/pseudofitmat3.R
Markov chain Monte Carlo sampler from Matern-III marked spatial point process
1 2 3 4 | pseudofitmat3(mat3, fname = c("centers.txt", "fingers.txt"),
win = owin(c(0, 1), c(0, 1)), R_clusters = 0.005, R_centers = 0.02,
N = 10000, seed = NULL, resultsName = NULL,
initialValues = pickInitialValues(), ...)
|
mat3 |
A mat3 object, see |
fname |
Will only be read if mat3 is set to NA. File names with the centers
and fingers dataset, to be read with |
win |
Square window in which the process is to be sampled. See |
R_clusters |
Nuisance parameter for the fingers' inhibition. No finger endings will be closer than R_clusters in Euclidean distance during the process sampling step (hard core inhibiition). |
R_centers |
Nuisance parameter for the centers' inhibition. At the birth-and-death process, process will take into account how many fingers (attached to other active centers) are currently at R_centers's distance from the candidate center being born. |
N |
Number of samples for integrating the area of the shadow. Defaults to 10000. |
seed |
Fixes the RNG seed for replication. Defaults to NULL, which does not fix the seed. |
resultsName |
... |
initialValues |
... |
... |
further arguments passed to |
fitmat3
returns a list containing at least the following components
parameters |
An L by 5 matrix with the samples from beta, phi, gamma, sigma, kappa. |
Guilherme Ludwig and Nancy Garcia
Garcia, N., Guttorp, P. and Ludwig, G. (2018) TBD
1 2 3 4 5 | set.seed(1234)
x <- rmat3(70, 2, 5, 0.05, 3)
plot(x)
# Changing default sampling sizes to make it run fast
model <- pseudofitmat3(x, N = 1000, seed = 1234)
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