createAlgorithm {MoNAn}

createAlgorithm

Description

Specifies the algorithm used in the estimation based on characteristics of the state and the effects.

Usage

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
)

Arguments

state

An object of class "processState.monan" that contains all relevant information about the outcome in the form of an edgelist, the nodesets, and covariates.

effects

An object of class "effectsList.monan" that specifies the model.

multinomialProposal

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.

burnInN1

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.

thinningN1

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.

iterationsN1

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.

gainN1

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.

burnInN2

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.

thinningN2

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.

initialIterationsN2

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.

nsubN2

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.

initGain

The magnitude of parameter updates in the first subphase of Phase 2. Values of around 0.2 (default) are recommended.

burnInN3

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.

thinningN3

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.

iterationsN3

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.

allowLoops

Logical: can individuals/resources stay in their origin?

Value

An object of class "algorithm.monan".

See Also

createProcessState(), createEffectsObject(), estimateMobilityNetwork()

Examples

# define algorithm based on state and effects characteristics
myAlg <- createAlgorithm(myState, myEffects, multinomialProposal = FALSE)