createAlgorithm: createAlgorithm

In MoNAn: Mobility Network Analysis

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,
  phase2Method = c("RM", "GT"),
  iterationsGT = NULL,
  maxIterationsGT = 10,
  finalIterationsGT = 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,
  phase2Method = c("RM", "GT"),
  iterationsGT = NULL,
  maxIterationsGT = 10,
  finalIterationsGT = 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. Only used if the estimation starts from zero, i.e., if no starting values are given and maximum pseudo-likelihood estimates are not available.
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?
phase2Method	How the parameters are estimated in Phase 2: "RM" (default) uses the Robbins-Monro stochastic approximation algorithm; "GT" uses Geyer-Thompson MCMC maximum likelihood estimation, as used for exponential random graph models. With "GT", the arguments nsubN2, initGain and initialIterationsN2 are not used.
iterationsGT	With phase2Method = "GT": the number of draws in each Geyer-Thompson iteration. Defaults to 10 * n_effects, but at least 100.
maxIterationsGT	With phase2Method = "GT": the maximum number of Geyer-Thompson iterations before the final step. Iterations stop earlier once the observed statistics lie within the range of the simulated ones. Defaults to 10.
finalIterationsGT	With phase2Method = "GT": the number of draws for the final Geyer-Thompson step, which determines the estimates. Defaults to 3 * iterationsGT.

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)

MoNAn documentation built on Oct. 11, 2026, 5:09 p.m.