This vignette will cover Monte Carlo realizations for modeling uncertainty using the rsyncrosim package within the SyncroSim software framework. For an overview of SyncroSim and rsyncrosim, as well as a basic usage tutorial for rsyncrosim, see the Introduction to rsyncrosim vignette.

SyncroSim Package: helloworldUncertainty

To demonstrate how to quantify model uncertainty using the rsyncrosim interface, we will need the helloworldUncertainty SyncroSim package. helloworldUncertainty was designed to be a simple package for introducing iterations to SyncroSim modeling workflows. The use of iterations allows for repeated simulations, known as “Monte Carlo realizations”, in which each simulation independently samples from a distribution of values.

The package takes from the user 3 inputs, mMean, mSD, and b. For each iteration, a value m, representing the slope, is sampled from a normal distribution with mean of mMean and standard deviation of mSD. The b value represents the intercept. These input values are run through a linear model, y=mt+b, where t is time, and the y value is returned as output.

Infographic of helloworldUncertainty package
Infographic of helloworldUncertainty package

For more details on the different features of the helloworldUncertainty SyncroSim package, consult the SyncroSim Enhancing a Package: Representing Uncertainty tutorial.


Install SyncroSim

Before using rsyncrosim you will first need to download and install the SyncroSim software. Versions of SyncroSim exist for both Windows and Linux.

Installing and loading R packages

You will need to install the rsyncrosim R package, either using CRAN or from the rsyncrosim GitHub repository. Versions of rsyncrosim are available for both Windows and Linux.

In a new R script, load the rsyncrosim package.

# Load R package for working with SyncroSim
## Warning: package 'rsyncrosim' was built under R version 4.3.3

Connecting R to SyncroSim using session()

Finish setting up the R environment for the rsyncrosim workflow by creating a SyncroSim Session object. Use the session() function to connect R to your installed copy of the SyncroSim software.

mySession <- session("path/to/install_folder")      # Create a Session based SyncroSim install folder
mySession <- session()                              # Using default install folder (Windows only)
mySession                                           # Displays the Session object
## class               : Session
## filepath [character]: C:/Program Files/SyncroSim
## silent [logical]    : TRUE
## printCmd [logical]  : FALSE
## condaFilepath [NULL]:

Use the version() function to ensure you are using the latest version of SyncroSim.

## [1] "2.5.11"

Installing SyncroSim packages using addPackage()

Install helloworldUncertainty using the rynscrosim function addPackage(). This function takes a package name as input and then queries the SyncroSim package server for the specified package.

# Install helloworldUncertainty
## Package <helloworldUncertainty> installed

helloworldUncertainty should now be included in the package list when we call the package() function:

# Get list of installed packages
##                    name                                 description version
## 1 helloworldUncertainty Example demonstrating how to use iterations   1.1.0
##                                                       location status
## 1 C:\\Users\\sarah\\SyncroSim\\Packages\\helloworldUncertainty     OK

Create a modeling workflow

When creating a new modeling workflow from scratch, we need to create objects of the following scopes:

For more information on these scopes, see the Introduction to rsyncrosim vignette.

Set up Library, Project, and Scenario

# Create a new Library
myLibrary <- ssimLibrary(name = "helloworldLibrary.ssim",
                         session = mySession,
                         package = "helloworldUncertainty",
                         overwrite = TRUE)

# Open the default Project
myProject = project(ssimObject = myLibrary, project = "Definitions")

# Create a new Scenario (associated with the default Project)
myScenario = scenario(ssimObject = myProject, scenario = "My first scenario")

View model inputs using datasheet()

View the Datasheets associated with your new Scenario using the datasheet() function from rsyncrosim.

# View all Datasheets associated with a Library, Project, or Scenario
##       scope                                  name                displayName
## 1   library                           core_Backup                     Backup
## 2   library                      core_CondaConfig        Conda Configuration
## 3   library                         core_JlConfig        Julia Configuration
## 4   library                       core_LNGPackage   Last Known Good Packages
## 5   library                  core_Multiprocessing            Multiprocessing
## 6   library                          core_Options                    Options
## 7   library             core_ProcessorGroupOption    Processor Group Options
## 8   library              core_ProcessorGroupValue     Processor Group Values
## 9   library                         core_PyConfig       Python Configuration
## 10  library                          core_RConfig            R Configuration
## 11  library                         core_Settings                   Settings
## 12  library                        core_SysFolder                    Folders
## 13  library                     corestime_Options            Spatial Options
## 14  project                       core_AutoGenTag       Auto Generation Tags
## 15  project               core_RunSchedulerOption      Run Scheduler Options
## 16  project             core_RunSchedulerScenario    Run Scheduler Scenarios
## 17  project                        core_StageName               Stage Groups
## 18  project                       core_StageValue            Stages by Group
## 19  project                      core_Transformer                     Stages
## 20  project                      corestime_Charts                     Charts
## 21  project            corestime_DistributionType              Distributions
## 22  project        corestime_ExternalVariableType         External Variables
## 23  project                    corestime_MapFacet               Map Faceting
## 24  project                        corestime_Maps                       Maps
## 25 scenario                  core_AutoGenTagValue Auto Generation Tag Values
## 26 scenario                         core_Pipeline                   Pipeline
## 27 scenario           corestime_DistributionValue              Distributions
## 28 scenario                    corestime_External                   External
## 29 scenario       corestime_ExternalVariableValue         External Variables
## 30 scenario             corestime_Multiprocessing    Spatial Multiprocessing
## 31 scenario  helloworldUncertainty_InputDatasheet             InputDatasheet
## 32 scenario helloworldUncertainty_OutputDatasheet            OutputDatasheet
## 33 scenario      helloworldUncertainty_RunControl                Run Control

From the list of Datasheets above, we can see that there are three Datasheets specific to the helloworldUncertainty package. Let’s view the contents of the input Datasheet as an R data frame.

# View the contents of the input Datasheet for the Scenario
datasheet(myScenario, name = "helloworldUncertainty_InputDatasheet")
## [1] mMean mSD   b    
## <0 rows> (or 0-length row.names)

Configure model inputs using datasheet() and addRow()

Input Datasheet

Currently our input Scenario Datasheet is empty! We need to add some values to our input Datasheet (InputDatasheet) so we can run our model. First, assign the contents of the input Datasheet to a new data frame variable using datasheet(), then check the columns that need input values.

# Load the input Datasheet to an R data frame
myInputDataframe <- datasheet(myScenario,
                              name = "helloworldUncertainty_InputDatasheet")

# Check the columns of the input data frame
## 'data.frame':    0 obs. of  3 variables:
##  $ mMean: num 
##  $ mSD  : num 
##  $ b    : num

The input Datasheet requires three values:

  • mMean : the mean of the slope normal distribution.
  • mSD : the standard deviation of the slope normal distribution.
  • b : the intercept of the linear equation.

Add these values to a new data frame, then use the addRow() function from rsyncrosim to update the input data frame.

# Create input data and add it to the input data frame
myInputRow <- data.frame(mMean = 2, mSD = 4, b = 3)
myInputDataframe <- addRow(myInputDataframe, myInputRow)

# Check values
##   mMean mSD b
## 1     2   4 3

Finally, save the updated R data frame to a SyncroSim Datasheet using saveDatasheet().

# Save input R data frame to a SyncroSim Datasheet
saveDatasheet(ssimObject = myScenario, data = myInputDataframe,
              name = "helloworldUncertainty_InputDatasheet")
## Datasheet <helloworldUncertainty_InputDatasheet> saved

RunControl Datasheet

The RunControl Datasheet provides information about how many time steps and iterations to use in the model. Here, we set the number of iterations, as well as the minimum and maximum time steps for our model. The number of iterations we set is equivalent to the number of Monte Carlo realizations, so the greater the number of iterations, the more accurate the range of output values we will obtain. Let’s take a look at the columns that need input values.

# Load RunControl Datasheet to a new R data frame
runSettings <- datasheet(myScenario, name = "helloworldUncertainty_RunControl")

# Check the columns of the RunControl data frame
## 'data.frame':    0 obs. of  3 variables:
##  $ MaximumIteration: num 
##  $ MinimumTimestep : num 
##  $ MaximumTimestep : num

The RunControl Datasheet requires the following 3 columns:

  • MaximumIteration : total number of iterations to run the model for.
  • MinimumTimestep : the starting time point of the simulation.
  • MaximumTimestep : the end time point of the simulation.

Note: A fourth hidden column, MinimumIteration, also exists in the RunControl Datasheet (default=1).

We’ll add this information to an R data frame and then add it to the Run Control data frame using addRow(). For this example, we will use only five iterations.

# Create run control data and add it to the run control data frame
runSettingsRow <- data.frame(MaximumIteration = 5,
                             MinimumTimestep = 1,
                             MaximumTimestep = 10)
runSettings <- addRow(runSettings, runSettingsRow)

# Check values
##   MaximumIteration MinimumTimestep MaximumTimestep
## 1                5               1              10

Finally, save the R data frame to a SyncroSim Datasheet using saveDatasheet().

# Save RunControl R data frame to a SyncroSim Datasheet
saveDatasheet(ssimObject = myScenario, data = runSettings,
              name = "helloworldUncertainty_RunControl")
## Datasheet <helloworldUncertainty_RunControl> saved

Run Scenarios

Setting run parameters with run()

We will now run our Scenario using the run() function in rsyncrosim. If we have a large model and we want to parallelize the run using multiprocessing, we can set the jobs argument to be a value greater than one. Since we are using five iterations in our model, we will set the number of jobs to five so each multiprocessing core will run a single iteration.

# Run the first Scenario we created
myResultScenario <- run(myScenario, jobs = 5)
## [1] "Running scenario [1] My first scenario"

Running the original Scenario creates a new Scenario object, known as a Results Scenario, that contains a read-only snapshot of the input Datasheets, as well as the output Datasheets filled with result data. We can view which Scenarios are Results Scenarios using the scenario() function from rsyncrosim.

# Check that we have two Scenarios, and one is a Results Scenario
##   ScenarioID ProjectID                                           Name IsResult
## 1          1         1                              My first scenario       No
## 2          2         1 My first scenario ([1] @ 19-Apr-2024 12:42 PM)      Yes
##   ParentID Owner       DateLastModified IsReadOnly MergeDependencies
## 1       NA   N/A 2024-04-19 at 12:42 PM         No                No
## 2        1   N/A 2024-04-19 at 12:43 PM         No                No
##   IgnoreDependencies AutoGenTags
## 1                 NA          NA
## 2                 NA          NA

View results

Viewing results with datasheet()

The next step is to view the output Datasheets added to the Result Scenario when it was run. We can load the result tables using the datasheet() function. In this package, the Datasheet containing the results is called “OutputDatasheet”.

# Results of first Scenario
resultsSummary <- datasheet(myResultScenario,
                            name = "helloworldUncertainty_OutputDatasheet")

# View results table
##   Iteration Timestep       y
## 1         1        1  2.2530
## 2         1        2  1.5061
## 3         1        3  0.7591
## 4         1        4  0.0121
## 5         1        5 -0.7348
## 6         1        6 -1.4818

Plotting uncertainty in the SyncroSim Windows User Interface

Now that we have run multiple iterations, we can visualize the uncertainty in our results. For this plot, we will plot the average y values over time, while showing the 20th and 80th percentiles.

To create a plot using the Results Scenario we just generated, open the current Library in the User Interface and sync the updates from rsyncrosim using the “refresh” button in the upper toolbar (circled in red below). All the updates made in rsyncrosim should appear in the User Interface. We can now add the Results Scenario to the Results Viewer and create our plot. For more information on generating plots in the User Interface, see the SyncroSim tutorials on creating and customizing charts.

Using rsyncrosim with the SyncroSim Windows User Interface to plot uncertainty
Using rsyncrosim with the SyncroSim Windows User Interface to plot uncertainty