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Specifies the algorithm used in the estimation based on characteristics of the state and the effects.
createAlgorithm(
state,
effects,
multinomialProposal = FALSE,
burnInN1 = NULL,
thinningN1 = NULL,
iterationsN1 = NULL,
gainN1 = 0.1,
burnInN2 = NULL,
thinningN2 = NULL,
initialIterationsN2 = 50,
nsubN2 = 4,
initGain = 0.6,
burnInN3 = NULL,
thinningN3 = NULL,
iterationsN3 = 500,
allowLoops = NULL
)monanAlgorithmCreate(
state,
effects,
multinomialProposal = FALSE,
burnInN1 = NULL,
thinningN1 = NULL,
iterationsN1 = NULL,
gainN1 = 0.1,
burnInN2 = NULL,
thinningN2 = NULL,
initialIterationsN2 = 50,
nsubN2 = 4,
initGain = 0.6,
burnInN3 = NULL,
thinningN3 = NULL,
iterationsN3 = 500,
allowLoops = NULL
)
An object of class "algorithm.monan".
An object of class "processState.monan" that contains all relevant information about the outcome in the form of an edgelist, the nodesets, and covariates.
An object of class "effectsList.monan" that specifies the model.
How should the next possible outcome in the simulation chains be sampled? If TRUE, fewer simulation steps are needed, but each simulation step takes considerably longer. Defaults to FALSE.
The number of simulation steps before the first draw in Phase 1. A recommended value is at least n_Individuals * n_locations if multinomialProposal = FALSE, and at least n_Individuals if multinomialProposal = TRUE which is set as default.
The number of simulation steps between two draws in Phase 1. A recommended value is at least 0.5 * n_Individuals * n_locations if multinomialProposal = FALSE, and at least n_Individuals if multinomialProposal = TRUE which is set as default.
The number of draws taken in Phase 1. A recommended value is at least 4 * n_effects which is set as default. If the value is too low, there will be an error in Phase 1.
The size of the updating step after Phase 1. A conservative value is 0, values higher than 0.25 are courageous. Defaults to 0.1.
The number of simulation steps before the first draw in Phase 1. A recommended value is at least n_Individuals * n_locations if multinomialProposal = FALSE, and at least n_Individuals if multinomialProposal = TRUE which is set as default.
The number of simulation steps between two draws in Phase 2. A recommended value is at least 0.5 * n_Individuals * n_locations if multinomialProposal = FALSE, and at least n_Individuals if multinomialProposal = TRUE which is set as default.
The number of draws taken in subphase 1 of Phase 2. For first estimations, a recommended value is around 50 (default to 50). Note that in later subphases, the number of iterations increases. If this is a further estimation to improve convergence, higher values (100+) are recommended.
Number of subphases in Phase 2. In case this is the first estimation, 4 subphases are recommended and set as default. If convergence in a previous estimation was close, then 1-2 subphases should be enough.
The magnitude of parameter updates in the first subphase of Phase 2. Values of around 0.2 (default) are recommended.
The number of simulation steps before the first draw in Phase 3. A recommended value is at least 3 * n_Individuals * n_locations if multinomialProposal = FALSE, and at least 3 * n_Individuals if multinomialProposal = TRUE which is set as default.
The number of simulation steps between two draws in Phase 3. A recommended value is at least n_Individuals * n_locations if multinomialProposal = FALSE, and at least 2 * n_Individuals if multinomialProposal = TRUE which is set as default. In case this value is too low, the outcome might erroneously indicate a lack of convergence.
Number of draws in Phase 3. Recommended are at the very least 500 (default). In case this value is too low, the outcome might erroneously indicate a lack of convergence.
Logical: can individuals/resources stay in their origin?
createProcessState(), createEffectsObject(), estimateMobilityNetwork()
# define algorithm based on state and effects characteristics
myAlg <- createAlgorithm(myState, myEffects, multinomialProposal = FALSE)
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