Adds a paramter to factor collection

Description

Builds up the factor collection.

Usage

AddFactor(factors = c(), lambda = "qunif", name, min, max,
  int = FALSE)

Arguments

Value

The collection of created factors

Examples

## Not run: 
   f<- AddFactor(name="Age",min=20,max=60)
   f<- AddFactor(factors=f, name="Weight",min=50,max=120)
## End(Not run)

AddFactor0

Description

Creates or appends the factor collection

Usage

AddFactor0(factors = c(), ...)

Arguments

Value

The factor collection

Examples

## Not run: 
   f<- AddFactor0(name="Age",min=20,max=60)
   f<- AddFactor0(factors=f, name="Weight",min=50,max=120)
## End(Not run)

Concatenate results of multiple runs

Description

This function stores the output of the last model execution and it is intended to be used internally.

Usage

AddResults(d)

Arguments

AoE.Base

Description

The Design Of Experiments Base function

Usage

AoE.Base(m, factors = c(), fun = NULL)

Arguments

Value

The design matrix

AoE.CoV

Description

A simple funcion for calculate the Coefficient of Variation

Usage

AoE.CoV(d)

Arguments

Value

The coefficient of variation for data

AoE.ColumnCoV

Description

This function Calculates the relative squared deviation (RSD or CoV) for an used provided column name key in the parameter dataset.

Usage

AoE.ColumnCoV(dataset, key)

Arguments

Value

A data frame with Coefficient of variations

AoE.FullFactorial design generator

Description

Generate a Full Factorial sampling for evaluating the parameters of a model.

Usage

AoE.FullFactorial(n = 10, factors = c())

Arguments

Value

The Full Factorial design matrix for provided parameters

Examples

## Not run: 
 f<- AddFactor(name="cyclePoint",min=40,max=90)
 f<- AddFactor(factors=f, name="conjugationCost",min=1,max=80)
 d<- AoE.FullFactorial(2,f)
## End(Not run)

AoE.GetMorrisOutput

Description

Returns a dataframe holding the Morris result set

Usage

AoE.GetMorrisOutput(obj)

Arguments

Value

The results of Morris method

AoE.LatinHypercube

Description

Generate a LHS sample for model parameters

Usage

AoE.LatinHypercube(n = 10, factors = c(), convert = TRUE)

Arguments

Details

Generate the LHS sampling for evaluating the parameters of a model.

Value

The LHS design matrix for provided parameters

Examples

## Not run: 
 f<- AddFactor(name="cyclePoint",min=40,max=90)
 f<- AddFactor(factors=f, name="conjugationCost",min=1,max=80)
 d<- AoE.LatinHypercube(2,f)
## End(Not run)

AoE.MAE

Description

Calculates the average-error magnitude (MAE)

Usage

AoE.MAE(xs, xe)

Arguments

Value

The MAE value for provided datasets

AoE.Morris

Description

This is a wrapper for performing Morris's screening method on repast models. We rely on morris method from sensitivity package.

Usage

AoE.Morris(k = c(), p = 5, r = 4)

Arguments

References

Gilles Pujol, Bertrand Iooss, Alexandre Janon with contributions from Sebastien Da Veiga, Jana Fruth, Laurent Gilquin, Joseph Guillaume, Loic Le Gratiet, Paul Lemaitre, Bernardo Ramos and Taieb Touati (2015). sensitivity: Sensitivity Analysis. R package version 1.11.1. https://CRAN.R-project.org/package=sensitivity

AoE.NRMSD

Description

A simple Normalized Root-Mean-Square Deviation calculation using max and min values. NRMSD = RMSD(x) / (max(x) - min(x))

Usage

AoE.NRMSD(xs, xe)

Arguments

Value

The NRRMSD value for provided datasets

AoE.RMSD

Description

A simple Root-Mean-Square Deviation calculation.

Usage

AoE.RMSD(xs, xe)

Arguments

Value

The RMSD value for provided datasets

AoE.RandomSampling experiment desing generator

Description

Generate a Simple Random Sampling experiment design matrix.

Usage

AoE.RandomSampling(n = 10, factors = c())

Arguments

Value

The random sampling design matrix

Examples

## Not run: 
 f<- AddFactor(name="cyclePoint",min=40,max=90)
 f<- AddFactor(factors=f, name="conjugationCost",min=1,max=80)
 d<- AoE.RandomSampling(2,f)
## End(Not run)

AoE.Sobol

Description

This is a wrapper for performing Global Sensitivity Analysis using the Sobol Method provided by sensitivity package.

Usage

AoE.Sobol(n = 100, factors = c(), o = 2, nb = 100,
  fun.doe = AoE.LatinHypercube, fun.sobol = sobolmartinez)

Arguments

Details

This function is not intended to be used directly from user programs.

References

Gilles Pujol, Bertrand Iooss, Alexandre Janon with contributions from Sebastien Da Veiga, Jana Fruth, Laurent Gilquin, Joseph Guillaume, Loic Le Gratiet, Paul Lemaitre, Bernardo Ramos and Taieb Touati (2015). sensitivity: Sensitivity Analysis. R package version 1.11.1. https://CRAN.R-project.org/package=sensitivity

AoE.Stability

Description

This function verifies the stability of CoV for all columns given by parameter keys or all dataset columns if keys is empty.

Usage

AoE.Stability(dataset, keys = c())

Arguments

Value

A data frame with Coefficient of variations

Corrects the LHS design matrix

Description

Correct the LHS sampling matrix for a specific range applying the lambda function. The default value of 'lambda' is 'qunif'.

Usage

ApplyFactorRange(design, factors)

Arguments

Value

The corrected design matrix

Builds the simulation parameter set

Description

Merges the design matrix with parameters which will be keep fixed along simulation runs.

Usage

BuildParameterSet(design, parameters)

Arguments

Value

A data frame holding all parameters required for running the model

Examples

## Not run: 
   modeldir<- "c:/usr/models/BactoSim(HaldaneEngine-1.0)"
   e<- Model(modeldir=modeldir,dataset="ds::Output")
   Load(e)

   f<- AddFactor(name="cyclePoint",min=40,max=90)

   p<- GetSimulationParameters(e)

   d<- AoE.LatinHypercube(factors=f)

   p1<- BuildParameterSet(d,p)
## End(Not run)

Calibration.GetMemberKeys

Description

Gets the list of keys (the factor names)

Usage

Calibration.GetMemberKeys(obj)

Arguments

Value

The collection of keys

Calibration.GetMemberList

Description

Gets the member list value

Usage

Calibration.GetMemberList(obj, key, name)

Arguments

Value

The member list

Clear the results data.frame

Description

This function is called automatically every time Run method is called.

Usage

ClearResults()

Easy.Calibration

Description

Search for the best set of parameters trying to minimize the calibration function provided by the user. The function has to operational models, the first based on the experimental setup where all parameters are defined a priori and the second using optimization techniques. Currently the only supported optimization technique is the particle swarm optimization.

Usage

Easy.Calibration(m.dir, m.ds, m.time = 300, parameters, exp.n = 100,
  exp.r = 1, smax = 4, design = "lhs", FUN, default = NULL)

Arguments

Value

A list with holding experiment, object and charts

Examples

## Not run: 
 my.cost<- function(params, results) {
   criteria<- c()
   Rate<- AoE.RMSD(results$X.Simulated,results$X.Experimental)
   G<- AoE.RMSD(results$G.T.,52)
   total<- Rate + G
   criteria<- cbind(total,Rate,G)
   return(criteria)
 }
 
 Easy.Setup("/models/BactoSim")
 v<- Easy.Calibration("/models/BactoSim","ds::Output",360,
                       f,exp.n = 1000, exp.r=1, smax=4, 
                       design="mcs", my.cost)
 

## End(Not run)

Easy API for Morris's screening method

Description

This function wraps all calls to perform Morris method.

Usage

Easy.Morris(m.dir, m.ds, m.time = 300, parameters, mo.p, mo.r, exp.r,
  FUN, default = NULL)

Arguments

Value

A list with holding experimnt, object and charts

Easy API for running a model

Description

This function provides a simple wrapper for performing a single or replicated model execution with a single set of parameters.

Usage

Easy.Run(m.dir, m.ds, m.time = 300, r = 1, default = NULL)

Arguments

Easy API for Runnning Experiments

Description

This function provides a simple wrapper for performing experimental setups using a design matrix

Usage

Easy.RunExperiment(m.dir, m.ds, m.time = 300, r = 1, design, FUN,
  default = NULL)

Arguments

Value

The experiment results

Easy.Setup

Description

This function configures the deployment directory where logs and output dataset will be generated. By default the deployment directory will be created under the model installation directory. The output generated by the Repast model will be redirected to the SystemOut.log file.

Usage

Easy.Setup(model, multicore = FALSE, deployment = c())

Arguments

Details

If the deployment directory is empty the installation directory given by the parameter model is used instead as the base directory. The deployment directory is /rrepast-deployment/.

Easy.ShowModelParameters

Description

Returns the list current model parameters

Usage

Easy.ShowModelParameters(v)

Arguments

Value

The model parameters

Easy API for Sobol's SA method

Description

This functions wraps all required calls to perform Sobol method for global sensitivity analysis.

Usage

Easy.Sobol(m.dir, m.ds, m.time = 300, parameters, exp.n = 500,
  bs.size = 200, exp.r = 1, FUN, default = NULL,
  fsobol = sobol2002, fsampl = AoE.LatinHypercube)

Arguments

Value

A list with holding experimnt, object and charts

Easy API for output stability

Description

This functions run model several times in order to determine how many experiment replications are required for model's output being stable (i.e. the convergence of standard deviation)

Usage

Easy.Stability(m.dir, m.ds, m.time = 300, parameters, samples = 1,
  tries = 100, vars = c(), FUN, default = NULL)

Arguments

Value

A list with holding experiment, object and charts

Easy.getChart

Description

Returns the chart instance

Usage

Easy.getChart(obj, key)

Arguments

Value

The plot instance

Easy.getPlot

Description

Returns the chart instance

Usage

Easy.getPlot(obj, c, key)

Arguments

Value

The plot instance

Engine

Description

Creates an instance of Engine

Usage

Engine()

Details

This function creates an instance of Repast model wrapper class. Before invoking the function Engine, make sure that environment was correctly initialized.

Value

An onject instance of Engine class

Engine.Finish

Description

Performs a cleanup on a engine instance.Finalize and destroy repast controller data.

Usage

Engine.Finish(e)

Arguments

Engine.GetModelOutput

Description

Gets the model output data as a CSV String array. Calls the engine method GetModelOutput to drain model output data.

Usage

Engine.GetModelOutput(e)

Arguments

Value

An array of strings containing the model's output

Examples

## Not run: 
   d<- "c:/usr/models/your-model-directory"
   m<- Model(d)
   csv<- Engine.GetModelOutput(m)
## End(Not run)

Engine.LoadModel

Description

Loads the model's scenario files

Usage

Engine.LoadModel(e, f)

Arguments

Details

This function loads the scenario of a Repast Model and initialize de model.

Engine.RunModel

Description

Performs the execution of Repast model

Usage

Engine.RunModel(e)

Arguments

Engine.SetAggregateDataSet

Description

Sets the model's dataset

Usage

Engine.SetAggregateDataSet(e, k)

Arguments

Details

Configure a dataset with the desired output values to be "drained" by the function Engine.GetModelOutput.

Examples

## Not run: 
   d<- "C:/usr/models/your-model-directory"
   m<- Model(d)
   setAggregateDataSet(m,"dataset-name")
## End(Not run)

Engine.endAt

Description

Configure the maximun simulated time for the current model run

Usage

Engine.endAt(e, v)

Arguments

Returns the model id

Description

This function provides a wrapper to the method getId() from repast context. The id is basically a String with the currently instantiated model name.

Usage

Engine.getId(e)

Arguments

Engine.getParameter

Description

The function gets the value of model parameter k as java.lang.Object

Usage

Engine.getParameter(e, k)

Arguments

Value

The parameter value

Engine.getParameterAsDouble

Description

Get the value of model parameter k as java.lang.Double

Usage

Engine.getParameterAsDouble(e, k)

Arguments

Value

The parmeter value as double

Engine.getParameterAsNumber

Description

Get the value of model parameter k as java.lang.Number

Usage

Engine.getParameterAsNumber(e, k)

Arguments

Value

The parmeter value as number

Engine.getParameterAsString

Description

Get the value of model parameter k as java.lang.String

Usage

Engine.getParameterAsString(e, k)

Arguments

Value

The parameter value as string

Engine.getParameterNames

Description

Get the parameter names

Usage

Engine.getParameterNames(e)

Arguments

Details

Returns the names of all declared model's parameters in the parameter.xml file in the scenario directory.

Value

A collection of parameter names

Engine.getParameterType

Description

Returns the declared type of a Repast model parameter

Usage

Engine.getParameterType(e, k)

Arguments

Value

The parameter type string

Engine.resetModelOutput

Description

Resets the the model output holder

Usage

Engine.resetModelOutput(e)

Arguments

Engine.setParameter

Description

Set the value of model parameter

Usage

Engine.setParameter(e, k, v)

Arguments

GetFactorLevels

Description

Returns the fator's levels

Usage

GetFactorLevels(factors, name)

Arguments

Value

Levels

Examples

## Not run: 
   f<- AddFactor0(name="Age",levels=c(25,30,40,65))
   f<- AddFactor0(factors=f, name="Weight",levels=c(60,70,80,90))
   
   GetFactorLevels(factors=f, "Age")
## End(Not run)

Get the number of factors

Description

Returns the total number of factors

Usage

GetFactorsSize(factors)

Arguments

Value

The number of parameters in factors collection

Gets the output

Description

Returns the results of a model a data.frame from the last RUN. Should be used only if model replication is equal to 1, otherwise GetResults must be used.

Usage

GetOutput(e)

Arguments

Value

Returns a data.frame with output data

Examples

## Not run: 
   d<- "C:/usr/models/your-model-directory"
   m<- Model(d)
   ...
   data<- GetOutput(m)
## End(Not run)

Returns the model results

Description

Returns the model results

Usage

GetResults()

Gets the parameters

Description

Returns the current set of paramters used for the last model run.

Usage

GetResultsParameters()

Value

A data.frame with parameters of the model.

GetSimulationParameterType

Description

Returns the declared parameter type.

Usage

GetSimulationParameterType(e, k)

Arguments

Value

The parameter type as string

Gets the simulation parameters

Description

Returns a dataframe with the current set of input parameters for the last model run.

Usage

GetSimulationParameters(e)

Arguments

Value

A data frame with simulation parameters

GoToPreviousDir

Description

Returns to the saved work directory

Usage

GoToPreviousDir()

GoToWorkDir

Description

Changes the current work directory saving the previous one which is used in GoToPreviousDir. This function is called by Easy.Setup

Usage

GoToWorkDir()

The Scenario loader

Description

Loads the model's scenario. This function must be called before running the model.

Usage

Load(e)

Arguments

Examples

## Not run: 
   d<- "C:/usr/models/your-model-directory"
   m<- Model(d)
   Load(m)
## End(Not run)

Set the log level to INFO

Description

Configures the underlying logging system

Usage

Logger.setLevelInfo()

Set the log level to WARNING

Description

Configures the underlying logging system

Usage

Logger.setLevelWarning()

The easy API for model initilization

Description

Instantiate a repast model from the model dir without loading the scenario file.

Usage

Model(modeldir = "", maxtime = 300, dataset = "none", load = FALSE)

Arguments

Details

This is the entry point for model execution. Typically any model execution will start with this function which encapsulates all low level calls for model initialization. In order to perform simulations with repast from R code only Model and a few more function calls are required: Load, Run. Finally the output of model is managed with functions GetResults and SaveSimulationData.

Value

Returns the instance of repast model

References

[1] North, M.J., N.T. Collier, and J.R. Vos, "Experiences Creating Three Implementations of the Repast Agent Modeling Toolkit," ACM Transactions on Modeling and Computer Simulation, Vol. 16, Issue 1, pp. 1-25, ACM, New York, New York, USA (January 2006).

Examples

## Not run: 
   d<- "C:/usr/models/your-model-directory"
   m<- Model(d)
## End(Not run)

PB.close

Description

Close the progress bar descriptor

Usage

PB.close()

PB.disable

Description

Disable the progress bar visualization

Usage

PB.disable()

PB.enable

Description

Enables the progress bar visualization

Usage

PB.enable()

PB.get

Description

Gets the the progress bar descriptor

Usage

PB.get()

PB.init

Description

Initialize progress bar for model execution.

Usage

PB.init(psets, replications)

Arguments

PB.isEnabled

Description

Returns the global value indicating if progress bar is enabled.

Usage

PB.isEnabled()

Value

Boolean TRUE if progress bar must be shown

PB.pset

Description

Update pset value

Usage

PB.pset(v)

Arguments

PB.rnum

Description

Update run number value

Usage

PB.rnum(v)

Arguments

PB.set

Description

Ses the progress bar descriptor

Usage

PB.set(obj)

Arguments

PB.update

Description

Update progress bar

Usage

PB.update(r = NULL)

Arguments

ParallelClose

Description

Finalize the parallel execution environment for R/Repast

Usage

ParallelClose()

ParallelInit

Description

Initialize the parallel execution environment for R/Repast

Usage

ParallelInit()

ParallelRun

Description

Run simulations in parallel. This function executes the time steps of an instantiated model. The number of replications of model runs can be specified by the function parameter. The seed parameter may be omitted and will be generated internally. If provided, the seed collection, must contain the same number of r parameter.

Usage

ParallelRun(modeldir, datasource, maxtime, r = 1, seed = c(),
  design = NULL, default = NULL)

Arguments

Value

The model output dataset

Examples

## Not run: 
   md<- "/usr/models/your-model-directory"
   output<- ParallelRun(modeldir= md, maxtime = 360, dataset= ds, r=4)
## End(Not run) 

ParallellRunExperiment

Description

Run the model multiple times for different parameters given by design matrix function parameter.

Usage

ParallellRunExperiment(modeldir, datasource, maxtime, r = 1, design, FUN,
  default = NULL)

Arguments

Details

The FUN function must return zero for perfect fit and values greater than zero otherwise.

Value

A list with output and dataset

Examples

## Not run: 
   my.cost<- function(params, results) { # your best fit calculation, being 0 the best metric.  }
   d<- "/usr/models/your-model-directory"
   f<- AddFactor(name="cyclePoint",min=40,max=90)
   f<- AddFactor(factors=f, name="conjugationCost",min=1,max=80)
   d<- AoE.LatinHypercube(factors=f)
   v<- ParallellRunExperiment()
## End(Not run)

Plot of calibration

Description

Generate plot for parameter sets providing best fit

Usage

Plot.Calibration(obj, key, title = NULL)

Arguments

Value

The resulting ggplot2 plot object

Plot of Morris output

Description

Generate plot for Morris's screening method

Usage

Plot.Morris(obj, type, title = NULL)

Arguments

Value

The resulting ggplot2 plot object

Plot of Sobol output

Description

Generate plot for Sobol's GSA

Usage

Plot.Sobol(obj, type, title = NULL)

Arguments

Value

The resulting ggplot2 plot object

Plot stability of output

Description

Generate plot for visually access the stability of coefficient of variation as function of simulation sample size.

Usage

Plot.Stability(obj, title = NULL)

Arguments

Value

The resulting ggplot2 plot object

Results.GetCharts

Description

Simplify the access to the charts member

Usage

Results.GetCharts(obj)

Arguments

Value

The charts element inside results

Results.GetExperiment

Description

Simplify the access to the experiment member

Usage

Results.GetExperiment(obj)

Arguments

Value

The experiment element inside results

Results.GetObject

Description

Simplify the access to the object member

Usage

Results.GetObject(obj)

Arguments

Value

The object element inside results

Run simulations

Description

This function executes the time steps of an instantiated model. The number of replications of model runs can be specified by the function parameter. The seed parameter may be omitted and will be generated internally. If provided, the seed collection, must contain the same number of r parameter.

Usage

Run(e, r = 1, seed = c())

Arguments

Value

The model output dataset

Examples

## Not run: 
   d<- "C:/usr/models/your-model-directory"
   m<- Model(d)
   Load(m)
   Run(m,r=2) # or Run(m,r=2,seed=c(1,2))
## End(Not run)

Run an experimental setup

Description

Run the model multiple times for different parameters given by design matrix function parameter.

Usage

RunExperiment(e, r = 1, design, FUN)

Arguments

Details

The FUN function must return zero for perfect fit and values greater than zero otherwise.

Value

A list with output and dataset

Examples

## Not run: 
   my.cost<- function(params, results) { # your best fit calculation, being 0 the best metric.  }
   d<- "c:/usr/models/your-model-directory"
   m<- Model(d,dataset="ds::Output")
   Load(m)
   f<- AddFactor(name="cyclePoint",min=40,max=90)
   f<- AddFactor(factors=f, name="conjugationCost",min=1,max=80)
   d<- LatinHypercube(factors=f)
   p<- GetSimulationParameters(e)
   exp.design<- BuildParameterSet(d,p)
   v<- RunExperiment(e,r=1,exp.design,my.cost) 
## End(Not run)

Saving simulation output

Description

Saves the simulation results of last call to Run(e) function.

Usage

SaveSimulationData(as = "csv", experiment = NULL)

Arguments

Details

The model must have been initialized or user must call setId explicitelly.

Value

The id of saved data

SequenceItem

Description

Generate a sequence from min to max using an increment based on the number of of elements in v

Usage

SequenceItem(v, min, max)

Arguments

Value

A sequence between min and max value

Stores a data.frame

Description

Stores a data.frame

Usage

SetResults(d)

Arguments

Sets the parameters

Description

Save the current set of paramters used for the last model run.

Usage

SetResultsParameters(d)

Arguments

SetSimulationParameter

Description

Modify model's default parameter collection

Usage

SetSimulationParameter(e, key, value)

Arguments

Set parameters for running model

Description

Modify the repast model parameters with values provided in parameter 'p' which is a data frame with just one row.

Usage

SetSimulationParameters(e, p)

Arguments

ShowClassPath

Description

Shows the current classpath

Usage

ShowClassPath()

Value

the current setting of JVM classpath

Examples

## Not run: 
   ShowClassPath()
## End(Not run)

ShowUsedCores

Description

Prints the number of cores used

Usage

ShowCores()

ShowModelPaths

Description

Prints the paths. Shows the directories currently used to load model scenario and lib. The output of this function is informational only and can be used to check whether model data is being loaded properly from correct locations.

Usage

ShowModelPaths()

Examples

## Not run: 
   ShowModelPaths()
## End(Not run)

UpdateDefaultParameters

Description

Modify the value of the default parameters which should be kept fixed

Usage

UpdateDefaultParameters(e, p)

Arguments

Examples

## Not run: 
   d<- "C:/usr/models/your-model-directory"
   m<- Model(d)
   Load(m)

   p<- c(name1=value1, name2=2)
   UpdateDefaultParameters(m,p)
## End(Not run)

WrapperRun

Description

Wrapper for the Run and ParallelRun functions

Usage

WrapperRun(modeldir, datasource, maxtime, r = 1, seed = c(),
  design = NULL, default = NULL, multi = TRUE)

Arguments

Value

The model output dataset

WrapperRunExperiment

Description

Wrapper for the RunExperiment and ParallelRunExperiment functions

Usage

WrapperRunExperiment(modeldir, datasource, maxtime, r = 1, design, FUN,
  default = NULL)

Arguments

Value

The model output dataset

check.integration

Description

Check if the integration jar library is correctelly installed in the model lib directory

Usage

check.integration(modelpath)

Arguments

Value

TRUE if the integration code is correctelly deployed

check.scenario

Description

Check if the scenario.xml is configured with the rrepast itegration code

Usage

check.scenario(modelpath)

Arguments

Value

TRUE if scenario is properly configured

col.sum

Description

Sum all columns but one (pset) of a data frame

Usage

col.sum(d, skip = c())

Arguments

Value

The original data frame with a new column (sum) holding the sum

config.check

Description

Verify if the installed model is correctelly configurated.

Usage

config.check(modelpath)

Arguments

Value

TRUE when all requisites are met

config.copylib

Description

Install or uninstall the integration jar file. This function manages the installation process of required jars to the model lib dir.

Usage

config.copylib(modelpath, uninstall = FALSE)

Arguments

Value

TRUE if install operation succed

config.scenario

Description

Add the integration library to the model's configuration

Usage

config.scenario(modelpath, uninstall = FALSE)

Arguments

Value

A logical TRUE if the model's scenario file has been modified

Create output directory

Description

A simple function to make a directory to save the model's data.

Usage

createOutputDir()

Details

Create the, if required, the directory to save the output data generate by the model. It is intended for internal use.

df2matrix

Description

This function converts data frames to matrix data type.

Usage

df2matrix(d, n = c())

Arguments

Value

The data frame converted to a matrix

dffilterby

Description

Selects a subset of a data frame, filtering by column values.

Usage

dffilterby(d, key, values = c())

Arguments

Value

The filtered data set

dfround

Description

Round all numeric columns of a data frame

Usage

dfround(d, p)

Arguments

Value

A data frame with rounded columns

dfsumcol

Description

Sum data frame columns but tho

Usage

dfsumcol(d, lst = c(), invert = FALSE)

Arguments

Value

The original data frame with a new column (sum) holding the sum

enginestats.calls

Description

Return the current calls to the 'Engine.RunModel' function

Usage

enginestats.calls(increment = FALSE)

Arguments

Value

The number of calls to 'Engine.RunModel'

enginestats.reset

Description

Reset internal statistics

Usage

enginestats.reset()

Helper function to get experiment dataset

Description

The RunExperiment function returns a list holding the paramset, output and dataset collection. The paramset collection contains the parameters used for running the experimental setup. The output has the results from user provided calibration function. The dataset collection has the raw output of 'Repast' aggregated dataset.

Usage

getExperimentDataset(e)

Arguments

Value

The reference to dataset container.

Helper function to get experiment output

Description

The RunExperiment function returns a list holding the paramset, output and dataset collection. The paramset collection contains the parameters used for running the experimental setup. The output has the results from user provided calibration function. The dataset collection has the raw output of 'Repast' aggregated dataset.

Usage

getExperimentOutput(e)

Arguments

Value

The reference to output container.

Helper function to get experiment paramset

Description

The RunExperiment function returns a list holding the paramset, output and dataset collection. The paramset collection contains the parameters used for running the experimental setup. The output has the results from user provided calibration function. The dataset collection has the raw output of 'Repast' aggregated dataset.

Usage

getExperimentParamSet(e)

Arguments

Value

The reference to output container.

Examples

## Not run: 
   d<- "C:/usr/models/your-model-directory"
   m<- Model(d)
   ...
   e<- RunExperiment(e,r=1,exp.design,my.cost)
   p<- getExperimentParamSet(e)
## End(Not run)

Gets the model name

Description

Provides the name of the model currently instantiated.

Usage

getId()

Gets Repast randomSeed name

Description

Returns the Repast randomSeed parameter name.

Usage

getKeyRandom()

Value

A string value holding the randomSeed name.

getLogDir()

Description

Returns the value for log directory

Usage

getLogDir()

Gets output directory

Description

Returns the value of module variable for storing the current output directory.

Usage

getOutputDir()

getpkgcores

Description

Returns the maximum number of cores to be used in parallel computations

Usage

getpkgcores()

Value

The number of cores

getpkgdefaultcores

Description

Provides the package default parallelism level which is 80% of total cores available

Usage

getpkgdefaultcores()

Value

Cores used by R/Repast

hybrid.distance

Description

Calculates the distance between some value a reference target value. It is an hybrid distance because when the value falls whithin a reference range the distance is 0, otherwise the distance between the value and the reference value is calculated using the user provided distance function.

Usage

hybrid.distance(value, reference, FUN = AoE.NRMSD)

Arguments

Value

The distance metric

hybrid.value

Description

A simple helper function for generating the input list for the function 'hybrid.distance'. This list must hold the value and a range centered over the value.

Usage

hybrid.value(value, distance)

Arguments

Value

The list holding the value and the interval 'min — value — max'

jarfile

Description

The jarfile returns the full path to some jar file available inside rrpast package

Usage

jarfile(fjar)

Arguments

Value

The full path to jar file

jvm.enablejmx

Description

Enable jmx for the current R/rJava session

Usage

jvm.enablejmx()

Details

Configures the JMX subsystem for the current session of R/rJava. This function must be called before any other function which initializes r/Java such as Easy.Setup or Model otherwise it will have no effect.

Examples

## Not run: 
   jvm.enablejmx()
## End(Not run)

jvm.get_parameters

Description

Returns the current java virtual machine parameters

Usage

jvm.get_parameters()

Value

A string with JVM parameters.

jvm.getruntime

Description

A wrapper for System.getRuntime()

Usage

jvm.getruntime()

Details

A simple wrapper for System.getRuntime() java method

Init R/JVM environment

Description

Initialize rJava and repast environment with classpath. This function is called internally and it is not meant to be used directlly.

Usage

jvm.init()

Details

The default parameters can be changed as needed calling the primitive jvm.set_parameters befor instantiating the model engine.

References

[1] rJava: Low-Level R to Java Interface. Low-level interface to Java VM very much like .C/.Call and friends. Allows creation of objects, calling methods and accessing fields.

Examples

## Not run: 
     jvm.init()
## End(Not run)

jvm.memory

Description

JVM memory state

Usage

jvm.memory()

Details

Provides information about the memory used by the JVM subsystem

jvm.resetOut

Description

Reset the System.out filed value to console output

Usage

jvm.resetOut()

Examples

## Not run: 
   jvm.resetOut()
## End(Not run)

jvm.runtimegc

Description

A wrapper for Runntime.gc()

Usage

jvm.runtimegc()

Details

Forces the execution of the JVM garbage collector

jvm.setOut

Description

Set the System.out filed to a file

Usage

jvm.setOut(f)

Arguments

Examples

## Not run: 
   jvm.setOut("/tmp/SysteOut.log")
## End(Not run)

jvm.set_parameters

Description

Configures the jvm parameters

Usage

jvm.set_parameters(s)

Arguments

Details

Set the underlying parameters for java virtual machine. The default values are "-server -Xms1024m -Xmx1024m". These defaults can be changed to fit the model requirements.

Examples

## Not run: 
   jvm.set_parameters("-server -Xms512m -Xmx2048m")
## End(Not run)

lcontains

Description

Cheks if a list contains a name

Usage

lcontains(l, n)

Arguments

Value

Boolean TRUE if name is found on list

get

Description

Retrieve the value for a list item

Usage

lget(l, n)

Arguments

Value

The item value

parallelize

Description

Tells R/Repast to use multicore. Default is using just one core.

Usage

parallelize(v = NULL)

Arguments

Value

Boolean with current state

pick.fittest

Description

Choose the best solutions minimizing the objective function

Usage

pick.fittest(out, goals = c(), n = 4)

Arguments

Value

The n rows holding the best results

Sets the model name

Description

Set the name of the model currently instantiated.

Usage

setId(s)

Arguments

Sets Repast randomSeed name

Description

Configures a non-default value for Repast randomSeed parameter name.

Usage

setKeyRandom(k)

Arguments

Sets output directory

Description

Configure the desired directoy to save model output data.

Usage

setOutputDir(s)

Arguments

setpkgcores

Description

Configures the maximum number of cores to be used in parallel computations

Usage

setpkgcores(v)

Arguments