Package 'MoNAn'

addEffect

Description

A function to add addtional effects to a moman effects object

Usage

addEffect(effectsObject, effectName, ...)

Arguments

effectsObject	The monan Effects object to which another effect should be added.
effectName	The name of the effect that should be added (e.g. loops).
...	Additional parameters of the effect, for example alpha, attribute.index, or resource.attribute.index

Value

An object of type effectsList.monan

Examples

# Create effects object and add effects
myE1 <- createEffects(myState)
myE1 <- addEffect(myE1, loops)
myE1 <- addEffect(myE1, reciprocity_basic)
myE1 <- addEffect(myE1, effectName = same_covariate, attribute.index = "region")

# Or simpler
myE1 <- createEffects(myState) |>
  addEffect(loops) |>
  addEffect(reciprocity_basic) |>
  addEffect(same_covariate, attribute.index = "region")

---

autoCorrelationTest

Description

The autoCorrelationTest indicates the degree to which the values of the dependent variable of consecutive draws from the chain in phase 3 are correlated. Here lower values are better. Values above 0.5 are very problematic and indicate that a higher thinning is needed.

Usage

autoCorrelationTest(ans)

Arguments

ans	An object of class "result.monan" resulting from an estimation with the function estimateMobilityNetwork().

Value

A number indicating the auto-correlation.

Examples

# regression diagnostics
autoCorrelationTest(myResDN)

---

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)

---

createEdgelist

Description

Creates an edgelist object, which is the standard format of the outcome to be modelled by MoNAn.

Usage

createEdgelist(el, nodeSet = NULL, nodes = NULL, edges = NULL)

monanDependent(el, nodeSet = NULL, nodes = NULL, edges = NULL)

Arguments

el	An edgelist in the form of a matrix with two columns and N rows. The first column indicates the origin of a person/resource, the second row the destination. Each row represents one observation.
nodeSet	The nodesets of the edgelists. This is a vector with three entries referencing the names of the nodesets of locations and individuals of the form c(location, location, individuals).
nodes	Alternative way to specify the nodeSet by naming nodes and edges: nodes denote the locations in the edgelist
edges	Alternative way to specify the nodeSet by naming nodes and edges: edges denote the individuals in the edgelist

Value

An object of class "edgelist.monan".

See Also

createProcessState()

Examples

# create an object of class edgelist.monan
transfers <- createEdgelist(mobilityEdgelist, c("organisations", "organisations", "people"))

---

createEffects

Description

Generates an empty effects object to which new effects can be added consecutively

Usage

createEffects(state)

Arguments

state

The state to which the model applies.

Value

An empty effects object of class effectsList.monan

Examples

#' myE1 <- createEffects(myState)

---

createEffectsObject

Description

Specifies the model with endogenous and exogenous predictors. The predictors in the model are called “effects”.

Usage

createEffectsObject(effectInit, checkProcessState = NULL)

Arguments

effectInit	A list of "effects", where each effect to be included is specified as a further list that contains the effect name and the additional parameters it needs. Effects without further parameters only contain the effect name (e.g., loops).
checkProcessState	For internal use only.

Value

An object of class "effectsList.monan".

Examples

# create an effects object
myEffects <- createEffectsObject(
  list(
    list("loops"),
    list("reciprocity_min"),
    list("dyadic_covariate", attribute.index = "sameRegion"),
    list("alter_covariate", attribute.index = "size"),
    list("resource_covar_to_node_covar",
      attribute.index = "region",
      resource.attribute.index = "sex"
    ),
    list("loops_resource_covar", resource.attribute.index = "sex")
  )
)

---

createNetwork

Description

Defines a network between locations, generally to be used as a predictor in the model. NOTE: The outcome variable of the model is not defined as a network, but as an edgelist!

Usage

createNetwork(
  m,
  isSymmetric = FALSE,
  isBipartite = FALSE,
  nodeSet = NULL,
  nodes = NULL
)

dyadicCovar(
  m,
  isSymmetric = FALSE,
  isBipartite = FALSE,
  nodeSet = NULL,
  nodes = NULL
)

Arguments

m	A square matrix containing the network data.
isSymmetric	Currently not in use.
isBipartite	Currently not in use.
nodeSet	Which nodeset are the nodes of the network. Usually this will be the locations in the data.
nodes	Alternative way to specify the nodeSet by naming nodes: nodes denote the locations in the edgelist

Value

An object of class "network.monan".

See Also

createProcessState(), createEdgelist()

Examples

# create an object of class network.monan
sameRegion <- outer(orgRegion, orgRegion, "==") * 1
sameRegion <- createNetwork(sameRegion, nodeSet = c("organisations", "organisations"))

---

createNodeSet

Description

Determines and names the nodesets of individuals and locations that make up the mobility network.

Usage

createNodeSet(x = NULL, isPresent = NULL, considerWhenSampling = NULL)

monanEdges(x = NULL, isPresent = NULL, considerWhenSampling = NULL)

monanNodes(x = NULL, isPresent = NULL, considerWhenSampling = NULL)

Arguments

x	Either a single number indicating how many items are in this nodeset or a vector from 1:n_items.
isPresent	Currently not in use.
considerWhenSampling	A boolean/logical vector of the length of the nodeset. Only in use in special cases. If the nodeset indicates a location, considerWhenSampling indicates whether the location is a possible destination, or is only an origin (e.g. a training facility). Entries in the vector of locations that cannot be a destination are FALSE. If the nodeset indicates mobile individuals, considerWhenSampling indicates whether their mobility should be modelled or whether it is structurally determined, that is, their mobility is exogenously defined and does not follow the same logic as the mobility of everybody else.

Value

An object of class "nodeSet.monan".

See Also

createProcessState()

Examples

# create an object of class nodeSet.monan
people <- createNodeSet(1:nrow(mobilityEdgelist))
organisations <- createNodeSet(length(orgRegion))

---

createNodeVariable

Description

Assigns a covariate to one nodeset, i.e., an exogenous characteristic of mobile individuals/resources or locations.

Usage

createNodeVariable(
  values,
  range = NULL,
  nodeSet = NULL,
  nodes = NULL,
  edges = NULL,
  addSame = NULL,
  addSim = NULL
)

monadicCovar(
  values,
  range = NULL,
  nodeSet = NULL,
  nodes = NULL,
  edges = NULL,
  addSame = NULL,
  addSim = NULL
)

Arguments

values	A vector assigning the covariate value to each element of the nodeset.
range	The range of possible values, currently not in use.
nodeSet	The nodeset to which the covariate applies.
nodes	Alternative way to specify the nodeSet by naming nodes or edges: nodes denote the locations in the edgelist
edges	Alternative way to specify the nodeSet by naming nodes or edges: edges denote the individuals in the edgelist
addSame	Will the variable be used to model categorical homophily (e.g., with the same_covariate effect)? In this case, addSame needs to be set to TRUE.
addSim	Will the variable be used to model continuous homophily (e.g., with the sim_covariate effect)? In this case, addSim needs to be set to TRUE.

Value

An object of class "nodeVar.monan".

See Also

createProcessState()

Examples

# create an object of class nodeVar.monan
region <- createNodeVariable(orgRegion, nodeSet = "organisations")
size <- createNodeVariable(orgSize, nodeSet = "organisations", addSim = TRUE)
sex <- createNodeVariable(indSex, nodeSet = "people")

---

createProcessState

Description

Creates the "Process state", i.e., a MoNAn object that stores all information about the data that will be used in the estimation. This includes the outcome variable (edgelist), the nodesets, and all covariates.

Usage

createProcessState(elements, dependentVariable)

Arguments

elements	A named list of the outcome variable (edgelist), the nodesets, and all covariates that contain the information about the data that will be used in the estimation.
dependentVariable	The name of the outcome variable (edgelist) as specified under "elements". This indicates what outcome the researcher is interested in.

Value

An object of class "processState.monan".

See Also

createEdgelist(), createNodeSet(), createNodeVariable(), createNetwork()

Examples

# Create a process state out of the mobility data objects:
# create objects (which are later combined to the process state)
transfers <- createEdgelist(mobilityEdgelist,
  nodeSet = c("organisations", "organisations", "people")
)
people <- createNodeSet(1:nrow(mobilityEdgelist))
organisations <- createNodeSet(1:length(orgRegion))
sameRegion <- outer(orgRegion, orgRegion, "==") * 1
sameRegion <- createNetwork(sameRegion,
  nodeSet = c("organisations", "organisations")
)
region <- createNodeVariable(orgRegion, nodeSet = "organisations")
size <- createNodeVariable(orgSize, nodeSet = "organisations", addSim = TRUE)
sex <- createNodeVariable(indSex, nodeSet = "people")

# combine created objects to the process state
myState <- createProcessState(list(
    transfers = transfers,
    people = people,
    organisations = organisations,
    sameRegion = sameRegion,
    region = region,
    size = size,
    sex = sex),
  dependentVariable = "transfers")

---

createWeightedCache

Description

Since MoNAn version 1.0.0, this function no longer exists.

Usage

createWeightedCache(processState, resourceCovariates = NULL)

Arguments

processState	Outdated.
resourceCovariates	Outdated.

Value

Outdated.

---

estimateMobilityNetwork

Description

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

Usage

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.

Value

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.

See Also

createProcessState(), createEffectsObject(), createAlgorithm()

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

---

extractTraces

Description

This function shows the values of simulated statistics in Phase 3 for subsequent draws from the chain. Ideally, the plots show points randomly scattered around the red line, which indicates the statistics in the data.

Usage

extractTraces(ans, effects)

## S3 method for class 'traces.monan'
plot(x, ...)

Arguments

ans	An object of class "result.monan" resulting from an estimation with the function estimateMobilityNetwork().
effects	An object of class "effectsList.monan" used in the estimation.
x	An object of class "traces.monan".
...	Additional plotting parameters, use not recommended.

Value

The function extractTraces returns a list that includes (1) the observed statistics for all effects, (2) the distribution of statistics for all simulations and (3) effect names. It is recommended to use the plotting function to inspect the traces.

The function plot.traces.monan shows a scatter plot of the statistics of simulated networks from phase three of the esimtation.

See Also

createEffectsObject()

Examples

# regression diagnostics
traces <- extractTraces(myResDN, myEffects)


plot(traces)

---

getMultinomialStatistics

Description

One updating step in simulating the mobility network model can be expressed as a multinomial logit model. Extracting the statistics for such a model allows a straight-forward estimation of a multinomial logit model to get initial estimates for the full mobility model, which increases the chances of model convergence in the first run of the estimation considerably.

Usage

getMultinomialStatistics(state, effects, cache = NULL)

Arguments

state	An object of class "processState.monan" that stores all information to be used in the model.
effects	An object of class "effectsList.monan" for which the statistics of a multinomial model should be calculated.
cache	Outdated parameter, no need to specify.

Value

A data frame with N * M rows (N = mobile individuals, M = number of locations) that specifies for each observation the statistics associated with moving to this location.

See Also

createProcessState(), createEffectsObject()

Examples

myStatisticsFrame <- getMultinomialStatistics(myState, myEffects)

---

gofMobilityNetwork

Description

Akin to ERGMs, goodness of fit testing is available to see whether auxiliary statistics are well captured by the model. The logic behind gof testing for network models is outlined in Hunter et al. (2008) and Lospinoso and Snijders (2019).

Usage

gofMobilityNetwork(ans, gofFunction, lvls = NULL, simulations = NULL)

gofDistributionNetwork(ans, gofFunction, lvls = NULL, simulations = NULL)

monanGOF(ans, gofFunction, lvls = NULL, simulations = NULL)

## S3 method for class 'gof.stats.monan'
plot(x, lvls, ...)

Arguments

ans	An object of class "result.monan" resulting from an estimation with the function estimateMobilityNetwork() using the option deps = TRUE.
gofFunction	A gof function that specifies which auxiliary outcome should be used, e.g., "getIndegree" or "getTieWeights".
lvls	The values for which the gofFunction should be calculated/plotted.
simulations	outdated parameter, no need to specify
x	An object of class "gof.stats.monan".
...	Additional plotting parameters, use discouraged.

Value

The function gofMobilityNetwork returns a list containing (1) the observed values of the auxiliary statistics and (2) a list of the simulated values of the auxiliary statistics.

The function plot.gof.stats.monan returns violin plots of the gof tests with observed values superimposed in red.

References

Hunter, D. R., Goodreau, S. M., & Handcock, M. S. (2008). Goodness of fit of social network models. Journal of the american statistical association, 103(481), 248-258.

Lospinoso, J., & Snijders, T. A. (2019). Goodness of fit for stochastic actor-oriented models. Methodological Innovations, 12(3).

See Also

getIndegree(), getTieWeights()

Examples

# goodness of fit
myGofIndegree <- gofMobilityNetwork(ans = myResDN, 
                                    gofFunction = getIndegree, 
                                    lvls = 1:100)

myGofTieWeight <- gofMobilityNetwork(ans = myResDN, 
                                     gofFunction = getTieWeights, 
                                     lvls = 1:30)


plot(myGofIndegree,  lvls = 20:70)
plot(myGofTieWeight, lvls = 1:15)

---

Example Data for the MoNAn Package

Description

These are example data for the MoNAn package and can be used to estimate a mobility network. The raw example data is synthetic (i.e., made up). This fictitious example contains 17 organisations representing a labour market that are located in two regions (north and south). 742 workers are employed in these organisations at two time-points. Some are mobile while others work in the same organisation at both time-points. The following objects are provided for this purpose:

mobilityEdgelist

The data frame indicates the origin at time 1 (first column) and the destination at time 2 (second column) for each of the 742 individuals between the 17 organisations. Note that some workers stay in their organisation, i.e. their origin equals their destination.

orgRegion

Categorical characteristic describing whether the organisation is located on the northern (1) or southern (0) region.

orgSize

Continuous measure representing the size of each organisation based on assets and revenue.

indSex

Individual-level characteristics representing sex.

Format

mobilityEdgelist A data frame with 742 rows and 2 columns.

orgRegion An object with 17 values.

orgSize An object with 17 values.

indSex An object with 742 values.

---

monanDataCreate

Description

A function to create a moman process state, i.e., a MoNAn object that stores all information about the data that will be used in the estimation. This includes the outcome variable (edgelist), the nodesets, and all covariates.

Usage

monanDataCreate(...)

Arguments

...	The monan objects to be included in the process State. This must include exactly one edgelist (dependent variable) and the two nodesets associated with the edgelist. Further allowed elements are (monadic or dyadic) covariates of locations and people

Value

An object of class "processState.monan".

Examples

#' # create objects (which are later combined to the process state)
transfers <- createEdgelist(mobilityEdgelist,
  nodeSet = c("organisations", "organisations", "people")
)
people <- createNodeSet(1:nrow(mobilityEdgelist))
organisations <- createNodeSet(1:length(orgRegion))
sameRegion <- outer(orgRegion, orgRegion, "==") * 1
sameRegion <- createNetwork(sameRegion,
  nodeSet = c("organisations", "organisations")
)
region <- createNodeVariable(orgRegion, nodeSet = "organisations")
size <- createNodeVariable(orgSize, nodeSet = "organisations", addSim = TRUE)
sex <- createNodeVariable(indSex, nodeSet = "people")

monanDataCreate(transfers, people, organisations, 
                sameRegion, region, size, sex)

---

Exemplary Outcome Objects for the MoNAn Package

Description

These are exemplary outcome objects for the MoNAn package and can be used in order not to run all precedent functions and thus save time. The following products are provided:

Format

myState An object of class "processState.monan" created by the function createProcessState().

myEffects An object of class "effectsList.monan" created by the function createEffectsObject() or createEffects().

myAlg An object of class "algorithm.monan" created by the function createAlgorithm().

myResDN An object of class "result.monan" created by the function estimateMobilityNetwork().

mySimDN An object of class "sims.monan" created by the function simulateMobilityNetworks().

---

print.effectsList.monan

Description

print.effectsList.monan

Usage

## S3 method for class 'effectsList.monan'
print(x, ...)

Arguments

x	An object of class "effectsList.monan".
...	For internal use only.

Value

The function print.effectsList.monan gives an overview of the specified effects.

Examples

myEffects

---

print.processState.monan

Description

print.processState.monan

Usage

## S3 method for class 'processState.monan'
print(x, ...)

Arguments

x	An object of class "processState.monan".
...	For internal use only.

Value

The function print.processState.monan gives an overview of the information included in the state object.

Examples

myState

---

scoreTest

Description

Based on an estimated model, a score-type test is available that shows whether statistics representing non-included effects are well represented. If this is not the case, it is likely that including them will result in significant estimates.

Usage

scoreTest(ans, effects)

## S3 method for class 'scoretest.monan'
print(x, ...)

Arguments

ans	An object of class "result.monan" resulting from an estimation with the function estimateMobilityNetwork().
effects	An object of class "effectsList.monan" in which the non included effects that should be tested are specified.
x	An object of class "scoretest.monan".
...	For internal use only.

Value

The function scoreTest returns basic values to calculate parametric and non-parametric p-values for each tested effect.

The function print.scoretest.monan shows parametric and non-parametric p-values for each tested effect.

See Also

createEffectsObject()

Examples

# test whether other effects should be included
myEffects2 <- createEffects(myState) |>
  addEffect(transitivity_min)

test_ME.2 <- scoreTest(myResDN, myEffects2)


test_ME.2

---

simulateMobilityNetworks

Description

Simulates mobility networks for given data, effects, and parameters. This function is mainly interesting to explore the behavior of the model or to do counter-factual simulations.

Usage

simulateMobilityNetworks(
  state,
  effects,
  parameters,
  allowLoops,
  burnin,
  thinning,
  nSimulations,
  cache = NULL
)

simulateDistributionNetworks(
  state,
  effects,
  parameters,
  allowLoops,
  burnin,
  thinning,
  nSimulations,
  cache = NULL
)

monanSimulate(
  state,
  effects,
  parameters,
  allowLoops,
  burnin,
  thinning,
  nSimulations,
  cache = NULL
)

Arguments

state	An object of class "processState.monan" that contains all relevant information about nodesets, and covariates. Further, an edgelist of the dependent variable needs to be specified with the initial mobility network as starting value for the simulation. For a large enough burn-in, any initial mobility network is allowed.
effects	An object of class "effectsList.monan" that specifies the model.
parameters	The parameters associated with the effects that shall be used in the simulations.
allowLoops	Logical: can individuals/resources stay in their origin?
burnin	The number of simulation steps that are taken before the first draw of a network is taken. A number too small will mean the first draw is influenced by the initially specified network. A recommended value for the lower bound is 3 * n_Individuals * n_locations.
thinning	The number of simulation steps that are taken between two draws of a network. A recommended value for the lower bound is n_Individuals * n_locations.
nSimulations	The number of mobility networks to be simulated.
cache	Outdated parameter, no need to specify.

Value

An object of class "sims.monan" with nSimulations entries, where each entry contains a further list with the state and the cache of the current simulation stored.

Examples

# simulate a mobility network
# note that thinning and burn-in values are for this example only
# in real cases, choose values aprrox. times 10
mySimDN <- simulateMobilityNetworks(
  myState,
  myEffects,
  parameters = c(2, 1, 1.5, 0.1, -1, -0.5),
  allowLoops = TRUE,
  burnin = 450,
  thinning = 150,
  nSimulations = 10
)

mySimDN[[1]]

---