estimateMobilityNetwork() R function from [MoNAn]

estimateMobilityNetwork

The core function of the package in which the model for the analysis of mobility tables is estimated.


estimateMobilityNetwork(
  state,
  effects,
  algorithm,
  initialParameters = NULL,
  prevAns = NULL,
  parallel = FALSE,
  cpus = 1,
  verbose = FALSE,
  returnDeps = FALSE,
  fish = FALSE,
  saveAlg = TRUE,
  cache = NULL
)

estimateDistributionNetwork(
  state,
  effects,
  algorithm,
  initialParameters = NULL,
  prevAns = NULL,
  parallel = FALSE,
  cpus = 1,
  verbose = FALSE,
  returnDeps = FALSE,
  fish = FALSE,
  saveAlg = TRUE,
  cache = NULL
)

monan07(
  state,
  effects,
  algorithm,
  initialParameters = NULL,
  prevAns = NULL,
  parallel = FALSE,
  cpus = 1,
  verbose = FALSE,
  returnDeps = FALSE,
  fish = FALSE,
  saveAlg = TRUE,
  cache = NULL
)

monanEstimate(
  state,
  effects,
  algorithm,
  initialParameters = NULL,
  prevAns = NULL,
  parallel = FALSE,
  cpus = 1,
  verbose = FALSE,
  returnDeps = FALSE,
  fish = FALSE,
  saveAlg = TRUE,
  cache = NULL
)

## S3 method for class 'result.monan'
print(x, covMat = FALSE, ...)

Arguments

state: An object of class "processState.monan" which 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.
algorithm: An object of class "algorithm.monan" which specifies the algorithm used in the estimation.
initialParameters: Starting values for the parameters. Using starting values, e.g., from a multinomial logit model (see getMultinomialStatistics()) increases the chances of model convergence in the first run of the estimation considerably.
prevAns: If a previous estimation did not yield satisfactory convergence, the outcome object of that estimation should be specified here to provide new starting values for the estimation.
parallel: Logical: computation on multiple cores?
cpus: Number of cores for computation in case parallel = TRUE.
verbose: Logical: display information about estimation progress in the console?
returnDeps: Logical: should the simulated values of Phase 3 be stored and returned? This is necessary to run GoF tests. Note that this might result in very large objects.
fish: Logical: display a fish?
saveAlg: Specify whether the algorithm object should be saved in the results object. Defaults to FALSE.
cache: Outdated parameter, no need to specify.
x: An object of class "result.monan".
covMat: Logical: indicating whether the covariance matrix should be printed.
...: For internal use only.

Returns

The function estimateMobilityNetwork returns an object of class "result.monan" that contains the estimates, standard errors, and convergence statistics. Furthermore, the covariance matrix used to calculate the standard errors is included, which also shows collinearity between effects. In case returnDeps = TRUE, the simulations of Phase 3 are included, too.

The function print.result.monan prints the results from a monan estimation with three columns indicating the estimate, the standard error, and the convergence statistic.

Examples


# estimate mobility network model

myAlg_short <- createAlgorithm(myState, myEffects, multinomialProposal = FALSE,
                               nsubN2 = 1, iterationsN3 = 100)

myResDN <- estimateMobilityNetwork(myState, myEffects, myAlg_short,
                                   initialParameters = NULL,
                                   # in case a pseudo-likelihood estimation was run, replace with
                                   # initialParameters = initEst,
                                   parallel = TRUE, cpus = 4,
                                   verbose = TRUE,
                                   returnDeps = TRUE,
                                   fish = FALSE
)

# check convergence
max(abs(myResDN$convergenceStatistics))

# view results
myResDN

myResDN

estimateMobilityNetwork function