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Bayesim

License: GPL v3 R-CMD-check Tests Codecov test coverage GH-Pages Lifecycle: experimental

A simulation framework for Bayesian models based on brms.

The main function is full_simulation with the main arguments being data_gen_confs, data_gen_fun, fit_confs and metrics. Bayesim will generate datasets by passing data_gen_confs rows to data_gen_fun and fit each model defined by fit_confs on each generated dataset. It then calculates all of the defined metrics for each model. This is, as of now, done in a fully crossed fashion.

Define Data Simulation

Data simulation consists of two parts. A data_gen_fun function and a data_gen_confs dataframe. Bayesim will feed each row of data_gen_confs into data_gen_fun to generate each individual dataset.

The only strictly necessary columns in data_gen_confs are:

  • dataset_N, the number of datasets that should be simulated per configuration. This is what Bayesim parallelize over.

  • id, a unique identifier string that is used to save the results per configuration row.

  • vars_of_interest, if you want metrics calculated for individual parameters. It should be a list of variable names used in data_gen_fun.

data_gen_fun should output a named list that contains the following parts:

  • dataset, a dataframe that is fed into brms::brm

  • testing_data, a dataframe that is used as newdata argument for certain out-of-sample metrics.

  • data_gen_output, a named list that contains all other information that you want to export from the data generating function. This should usually include all the input arguments (see example for how to get those easily), and a references list, that contains the reference values for all vars_of_interest variables (again, see the example for how to easily get those). An example is presented below:

```{r} constant_linpred_dgp <- function(data_N, data_link, data_family, seed = NULL, testing_data = TRUE, vars_of_interest = list(“mu”), mean = 0, …) { arguments <- as.list(c(as.list(environment()), list(…))) arguments$seed <- NULL

if (!is.null(seed)) { set.seed(seed) }

if (testing_data) { data_gen_size <- data_N * 2 } else { data_gen_size <- data_N } dataset <- data.frame() mu = rnorm(n = 1, mean = x, sd = 1) y = rnorm(n = data_gen_size, mean = mu, sd = 1)

# This creates a list of values for each of the vars_of_interest. arguments$references <- lapply( unlist(vars_of_interest), function(x) get(x) )

data_gen_output <- list() # Anything in addition to the function arguments you want to save about # the data generation process ie. If you are resampling the number # of invalid samples. ) data_gen_output <- c(data_gen_output, arguments)

if (testing_data) { return( list( dataset = list(y = dataset[1:data_N, ]), testing_data = list(y = dataset[(data_N + 1):data_gen_size, ]), data_gen_output = data_gen_output ) ) } else { return( list( dataset = dataset, testing_data = NULL, data_gen_output = data_gen_output ) ) } }

```

Define Fit Configurations

Fit configurations currently are dataframes with the following columns:

Define Metrics

Metrics are defined via a list of string identifiers. The supported metrics are:

Variable summaries

"v_mean" "v_sd" "v_median" "v_mad" "v_pos_prob" "v_quantiles" "v_bias" "v_rmse" "v_mae" "v_mse" "v_true_percentile"

Global MCMC Diagnostics

"divergent_transitions_rel" "divergent_transitions_abs" "rstar" "bad_pareto_ks" "pareto_k_values" "time_per_sample"

Variable MCMC Diagnostics

"rhat" "ess_bulk" "ess_tail"

Predictive Metrics

"elpd_loo" "elpd_loo_pointwise" "elpd_loo_pointwise_summary" "elpd_test" "elpd_test_pointwise_summary" "rmse_loo" "rmse_loo_pointwise" "rmse_loo_pointwise_summary" "rmse_test" "rmse_test_pointwise_summary" "r2_loo" "r2_loo_pointwise" "r2_loo_pointwise_summary" "r2_test" "r2_test_pointwise_summary"

Posterior sample based metrics

"log_lik_pointwise" "log_lik_summary" "ppred_summary_y_scaled" "ppred_pointwise" "residuals" "posterior_linpred" "posterior_linpred_transformed"

Observations

"y_pointwise" "y_pointwise_z_scaled" "y_summaries"

Data

"data_gen"

Fits

"fit_gen"

Or see metric_lookup for all currently implemented metrics.

Additional Arguments

seed, sets a seed that will result in the rest of the simulation happening deterministically, conditional on the seed. Allows for reproduction of individual results or the entire simulation run later on.

Output

  • result_path = "./", The path where the result .RDS files should be saved.

  • debug = FALSE, TRUE will save all intermediate results as .RDS files in the result_path directory to support debugging.

Stan Options

stan_pars should be a named list that contains the following arguments:

  • warmup, directly passed to brms::brm

  • iter, directly passed to brms::brm

  • chains, directly passed to brms::brm

  • init, directly passed to brms::brm

  • backend = "rstan", directly passed to brms::brm We recommend rstan due to instabilities of cmdstanr on clusters.

  • cmdstan_path, useful when working with cmdstan on a computing cluster where cmdstan might not be installed in the default location. Use the path to the main directory, eg "~/.cmdstan/cmdstan-2.29.2".

  • cmdstan_write_path, directory for cmdstan to write compiled model files to. This should not be a temporary directory as those might get cleaned up during the simulation run.

Using multiple Cores

  • ncores_simulation = 1, If set to more than 1, Bayesim will parallelize across datasets within each row of data_gen_confs using the specified number of processes.

  • cluster_type = "PSOCK", Defines the type of cluster used by the parallel package. Windows requires PSOCK however ’FORK` can save quite some time due to the repeated cluster setup times.

Bayesim has been used in the following projects: