Getting started with socialSim

Overview

The socialSim R package provides tools to simulate and analyse datasets of social interactions between individuals using hierarchical Bayesian models implemented in Stan.

This vignette demonstrates a typical workflow using three main functions:

1. simulate_data() – generate datasets of social interactions
   
2. run_model() – fit a Stan model to the simulated datasets
   
3. summarise_results() – evaluate bias and dispersion of estimated parameters

1. Simulating data

sim <- simulate_data(
  ind = 200,
  partners = 4,
  repeats = 1,
  iterations = 5,
  B_0 = 1,
  psi = 0.3,
  Valpha = 0.2,
  Vepsilon = 0.1
)

This creates a list of datasets representing repeated social interactions.
You can control study design components, variance components and correlations between direct and indirect effect.

2. Fitting a model

To analyse the data, fit one of the included Stan models:

res <- run_model(sim, model = "Trait.stan", iter = 2000, cores = 4)

Importantly, you will need rstan installed for this step.

3. Summarising results

Once the models are fitted, summarise bias and dispersion across simulations:

summary <- summarise_results(res)
print(summary)

This function extracts model estimates and computes metrics such as mean absolute deviation (MADm) across replicates.

Example output (simulated workflow)

Here’s a minimal example with few iterations for a fast runtime:

sim <- simulate_data(ind = 50, partners = 2, iterations = 4, Valpha = 0.2, Vepsilon = 0.1)
res <- run_model(sim, model = "Trait.stan", iter = 500, cores = 4)
summary <- summarise_results(res)
print(summary)

Conclusion

The socialSim package helps researchers design, simulate, and evaluate models of social phenotypes and indirect genetic effects.

?simulate_data
?run_model
?summarise_results

and visit the GitHub page for the latest updates.