EcoEnsemble is an R package to set up, fit and sample from the ensemble framework described in Spence et al (2018) for time series outputs.


You can install the development version of EcoEnsemble using the devtools package:



Fitting an ensemble model in EcoEnsemble is done in three main steps:

  1. Eliciting priors on discrepancy terms: This is done by using the EnsemblePrior() constructor.
  2. Fitting the ensemble model: Using the fit_ensemble_model() function with simulator outputs, observations and prior information. The ensemble model can be fit, obtaining either the point estimate, which maximises the posterior density, or running Markov chain Monte Carlo to generate a sample from the posterior denisty of the ensemble model.
  3. Sampling the latent variables from the fitted model: Using the generate_sample() function with the fitted ensemble object, the discrepancy terms and the ensemble’s best guess of the truth can be generated. Similarly to fit_ensemble_model(), this can either be a point estimate or a full sample.

We illustrate this process with datasets included with the package. EcoEnsemble comes loaded with the predicted biomasses of 4 species from 4 different mechanistic models of fish populations in the North Sea. It also includes statistical estimates of the biomasses from single-species stock assessments, and covariances for the model outputs and assessments. The models are run for different time periods and different species.

# Outputs from mizer. These are logs of the biomasses for each year in a simulation.
N.pout Herring Cod Sole
1984 10.31706 13.33601 10.80006 10.98139
1985 12.07673 13.63592 10.46646 10.87285
2049 12.46354 14.02923 12.27473 10.80954
2050 12.46509 14.03027 12.27422 10.81003

To encode prior beliefs about how model discrepancies are related to one another, use the EnsemblePrior() constructor. Default values are available.

priors <- EnsemblePrior(4)

or custom priors can be specified.

#Endoding prior beliefs. Details of the meanings of these terms can be found in the vignette or the documentation
num_species <- 4
priors <- EnsemblePrior(
  d = num_species,
  ind_st_params = IndSTPrior("lkj",  list(3, 2), 3, AR_params = c(1,1)),
  ind_lt_params = IndLTPrior(
    list(c(10,4,8, 7),c(2,3,1, 4)),
    list(matrix(5, num_species, num_species),
         matrix(0.5, num_species, num_species))
  sha_st_params = ShaSTPrior("inv_wishart",list(2, 1/3),list(5, diag(num_species))),
  sha_lt_params = 5,
  truth_params = TruthPrior(num_species, 10, list(3, 3), list(10, diag(num_species)))

This creates an EnsemblePrior object, which we can use to fit the ensemble model using the fit_ensemble_model() function and the data loaded with the package. When running a full MCMC sampling of the posterior, this step may take some time. Samples can then be generated from the resulting object using the generate_sample() function.

fit <- fit_ensemble_model(observations = list(SSB_obs, Sigma_obs),
                          simulators = list(list(SSB_ewe, Sigma_ewe, "EwE"),
                                            list(SSB_lm,  Sigma_lm,  "LeMans"),
                                            list(SSB_miz, Sigma_miz, "mizer"),
                                            list(SSB_fs,  Sigma_fs,  "FishSUMS")),
                          priors = priors)
samples <- generate_sample(fit)

This produces an EnsembleSample object containing samples of the ensemble model predictions. These can be viewed by calling the plot() function on this object. For a full MCMC sample, this includes ribbons giving quantiles of the ensemble outputs. If only maximising the posterior density, then only the single ouput is plotted.



Spence, M. A., J. L. Blanchard, A. G. Rossberg, M. R. Heath, J. J. Heymans, S. Mackinson, N. Serpetti, D. C. Speirs, R. B. Thorpe, and P. G. Blackwell. 2018. “A General Framework for Combining Ecosystem Models.” Fish and Fisheries 19: 1013–42.