Description Usage Arguments Value Author(s) Examples
Estimates potentials vias Stochastic Approximation algorithm.
1 2 3 |
X |
The observed random field (matrix). |
gModel |
A GibbsModel object (V and vMat can be NULL). |
type |
Model type. Either "general", "symetric" or "equal". Check GibbsMPLE for details. |
initial |
Wich method to get initial estimates. Currently only "MPLE" available. |
MC_size |
Number of fields to sample at each step. |
iter |
Maximum number of iterations. |
macrosteps |
Number of macrosteps per random field simulation. |
print_sample |
Indicates if the a sample of the current model should be printed each 10 steps. |
a0, a1, a2 |
constants to define size of each step. The gradient vector is multiplied by a0/(i*a1 + a2) |
A GibbsModel object estimated potentials.
Victor Freguglia Souza
1 | StocAp(example.X,example.GibbsModel,"symetric",MC_size = 5)
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