The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Simulate and Analyse Social Interaction Models
The socialSim R package provides tools to simulate and analyse datasets of social interactions between individuals using hierarchical Bayesian models implemented in Stan.
It enables users to generate realistic social interaction data, where
individual phenotypes influence and respond to those of their partners.
You can simulate a sampling design by adjusting the number of
individuals, partners, and repeated dyads. The simulation
framework allows control over variation in mean trait values,
social responsiveness, and social impact and correlation,
making it suitable for research on direct and indirect genetic
effects (DGEs and IGEs) and interacting
phenotypes. See ?simulate_data for a full list of
adjustable parameters.
The package also provides analysis functions to evaluate model performance in terms of bias and dispersion, using both established and novel approaches to modelling social effects, including impact–responsiveness, variance–partitioning, and trait-based models.
You can install the development version from GitHub using:
# install.packages("remotes")
remotes::install_github("RoriWijnhorst/socialSim")
# Then load the package:
library(socialSim)library(socialSim)
# 1. Simulate data. See ?simulate_data for all adjustable parameters
sim <- simulate_data(
ind = 1200, # number of unique focal individuals
partners = 4, # number of social partners per individual
repeats = 1, # number of repeats of dyads
iterations = 10, # number of datasets created
B_0 = 1, # population intercept
psi = 0.3, # population-level response
Valpha = 0.2, # variance in direct effects
Vepsilon = 0.1 # variance in residual partner effects
)
# 2. Fit a Stan model. For the analyses, cmdstanr or rstan needs to be installed.
# using cmdstanr will be faster, since it runs model in parallel on different cores of your PC.
res <- run_model(sim, model = "Trait.stan", iter=2000, cores = 6)
# 3. Summarise results
summary <- summarise_results(res)
print(summary)| Model name | Description |
|---|---|
| I&R.stan | Full impact–responsiveness model |
| VP.stan | Variance-partitioning model |
| Trait.stan | Trait-based model with residual partner effects |
| Trait_only.stan | Simple trait-based model without residual partner effects |
| Trait_RS.stan | Random-slope trait model with residual partner effects |
| Trait_EIV.stan | Errors-in-variable trait model with residual partner effects |
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.