Beyond spades

So far, we have run models with the spades function.

spades() only has a few arguments:

- debug
- .plotInitialTime
- .saveInitialTime
- progress
- cache

What if we want to do more stuff?


What if we have a stochastic model and need many independent (Monte Carlo) runs?

The experiment function does this.

?experiment # for details and examples

Generally, however, this creates problems because of the computational time required …


The replicates argument

It is as easy as replicates = 2 to do two replicates:

?experiment # for details

# See Example 5
#  ... run mySim from top of help file
sims <- experiment(mySim, replicates = 2)
attr(sims, "experiment")$expDesign # shows 2 reps of same expt

What does it return?

What is the attr?

The curse of replication

Replication is necessary but it multiplies the time it takes to run any model.

Fortunately, replication can use parallel processing:

  • This is the simplest, yet most effective, parallel processing;
  • No communication between individual simulations, until the end;
  • So they can be spread across threads on a single computer or among computers;
  • Requires a cluser (cl) argument.


  • SpaDES functions that are parallel aware:

    • experiment, POM, splitRaster
  • In each case, there are two ways:

## option 1:
raster::beginCluster() # make a cluster silently in background

## option 2:
cl <- parallel::makeCluster() # need to explicitly pass the
                              # cluster object into functions
                              # using cl argument

Try it!

experiment: Varying parameter values

## see example 1
experimentParams <- list(fireSpread = 
                           list(spreadprob = list(0.2, 0.23),
                                nFires = list(20, 10)),
                         caribouMovement = 
                           list(N = list(100, 1000)))
sims <- experiment(mySim, params = experimentParams)
attr(sims, "experiment")

experiment: Saving files

Often, a spades call will generate output files. Where do they go when using experiment?

## see example 4
sims <- experiment(mySim, 
                   params = experimentParams, 
                   dirPrefix = c("expt", "simNum"))
attr(sims, "experiment")$expVals # shows 8 alternative
               #experiment levels, 24 unique parameter values


Working through other examples of experiment


Pick a few examples and try to understand them. (NOTE: they get more complicated as you get towards the bottom.)


Running parallel simulations

Clearly, using experiment can take a lot of resources, but there are a few options:

  • Use your own computer, a local cluster, a server, cloud and more;

  • Access Compute Canada for free (though sometimes the queues are long):

Pattern Oriented Modeling

Estimating unknown parameters

  • Ecological models use equations and functions
  • These have parameters
  • Parameters can be estimated from:

    • directly from data (e.g., regression, machine learning)
    • indirectly using (e.g., pattern oriented modeling) (e.g., Grimm et al. Science 2005, Marucco & McIntire J Appl Ecol 2010)

Directly from data

  • (Just a brief discussion of this here)
  • Remember, we are using R!
  • So you can use any statistical method that R has
  • Mixed models, non-linear models, machine learning, bayesian
nTrees <- 50
diamCM <- rlnorm(nTrees, meanlog = 2, sdlog = 0.7)
height <- rnorm(nTrees, sqrt(diamCM)*2 + rnorm(nTrees))
glmObj <- glm(height ~ diamCM)
plot(diamCM, height)

Predict method

  • These methods often have predict methods associated with them
  • So, instead of using “fixed” parameters in simulation models
  • We can use these methods which give more accurate prediction:

    • given uncertainty,
    • random effects,
    • covariance matrix
# predict with new data
predVals <- predict(glmObj, newdata = data.frame(
  diamCM = rlnorm(nTrees, meanlog = 2, sdlog = 0.7)

Using this in simulation models

  • In R, everything is an object
  • The glmObj and predictVals are objects
  • These have many features that can be used in simulations

    • plot methods
    • standard errors, covariance matrices
    • random effects terms
  • For the first 25 years of simulation modeling in ecology, parameters were copied into simulation models and hard coded
  • This made it difficult to update the simulation model if there are new data and the regression needs to be re-fit
  • Using SpaDES we can now fit the data and use the parameters all inside the simulation

Indirectly from data

Pattern Oriented Modeling (POM)

  • What does this mean?

Heuristic Optimization

  • Optimization is the process of minimizing some objective function
  • Heuristic optimization is when there is no derivative-based solution
  • Requires simulation-based approaches
  • In R, currently, DEoptim seems to be the best optimization package for heuristic optimization
  • POM function in SpaDES uses this package


There are several examples in ?POM, which we will go through:

## example 1
# What is example 1 doing?
# Run it
# What does the result mean?

More complicated POM

  • What would be next?

More complicated POM - example 2

  • What would be next?
## example 2
# What is example 2 doing?
# Run it
# What does the result mean?

More complicated POM - example 3

  • What would be next?
## example 3 
# What is example 3 doing?
# Run it
# What does the result mean?

Often we want to run a simulation with a variety of parameter values as inputs. This is sometimes called a simulation experiment. The experiment function gives us an easy interface to this.

We will use the establishment module that we created in exercise 4a.

Download a module

module.path <- file.path(dirname(tempdir()), "modules")
downloadModule("establishment", path = module.path)
setPaths(modulePath = module.path)

modules <- list("establishment")

mySim <- simInit(modules = modules)

# make sure the plotting device is clear

Create an experiment

  1. Find out which parameters are in this module. (Hint: where is the module? Can you open it easily from R?)
  2. Pick one of the parameters and create a range of values for it.
  3. Following the structure indicated in example 4 of ?experiment, use experiment to run a spades call for each parameter set (note that experiment is just a wrapper around spades).
  4. Inspect the experiment structure using attributes(mySim).
  5. Create a plot showing on the x axis your parameter values, and on the y-axis something like sum(mySim$establish[]), i.e., the number of pixels that had an establishment event.

Advanced - Create a two-parameter experiment

  1. Repeat as above, but vary two parameters.

See a solution here