Skip to contents

Run Bayesian model

Source: R/run-model.R
run_model.Rd

Run Bayesian model

Usage

run_model(
  model_data,
  refresh = 100,
  chains = 4,
  parallel_chains = 4,
  iter_warmup = 1000,
  iter_sampling = 1000,
  adapt_delta = 0.95,
  max_treedepth = 14,
  k = NULL,
  output_basename = NULL,
  output_dir = ".",
  save_model = TRUE,
  overwrite = FALSE,
  set_seed = NULL,
  quiet = FALSE,
  jags_data,
  inits,
  parameters_to_save,
  track_n,
  n_adapt,
  n_burnin,
  n_thin,
  n_chains,
  n_saved_steps,
  n_iter,
  modules,
  parallel,
  n_cores,
  ...
)

Arguments

model_data

List. Model data generated by prepare_model().

refresh

Numeric. Passed to cmdstanr::sample(). Number of iterations between screen updates. If 0, only errors are shown.

chains

Numeric. Passed to cmdstanr::sample(). Number of Markov chains to run.

parallel_chains

Numeric. Passed to cmdstanr::sample(). Maximum number of chains to run in parallel.

iter_warmup

Numeric. Passed to cmdstanr::sample(). Number of warmup iterations per chain.

iter_sampling

Numeric. Passed to cmdstanr::sample(). Number of sampling (post-warmup) iterations per chain.

adapt_delta

Numeric. Passed to cmdstanr::sample(). The adaptation target acceptance statistic.

max_treedepth

Numeric. Passed to cmdstanr::sample(). The maximum allowed tree depth for the NUTS engine. See ?cmdstanr::sample.

k

Numeric. The K-fold group to run for cross-validation. Only relevant if folds defined by prepare_model(calculate_cv = TRUE) or custom definition. See ?prepare_model or the models article for more details.

output_basename

Character. Name of the files created as part of the Stan model run and the final model output RDS file if save_model = TRUE.

output_dir

Character. Directory in which all model files will be created.

save_model

Logical. Whether or not to save the model output to file as an RDS object with all required data. Defaults to TRUE.

overwrite

Logical. Whether to overwrite an existing model output file when saving

set_seed

Numeric. If NULL (default) no seed is set. Otherwise an integer number to be used with withr::with_seed() internally to ensure reproducibility.

quiet

Logical. Suppress progress messages? Default FALSE.

jags_data

Defunct.

inits

Defunct.

parameters_to_save

Defunct.

track_n

Defunct.

n_adapt

Defunct.

n_burnin

Defunct.

n_thin

Defunct.

n_chains

Defunct.

n_saved_steps

Defunct.

n_iter

Defunct.

modules

Defunct.

parallel

Defunct.

n_cores

Defunct.

...

Other arguments passed on to cmdstanr::sample()

Value

A list containing the model output (model_fit), meta data for the analysis (meta_data), meta data for the strata (meta_strata) and prepared data counts from prepare_data() (raw_data).

Details

The model is set up in prepare_model(). The run_model() function does the final (and often long-running) step of actually running the model. Here is where you can tweak how the model will be run (iterations etc.).

See the models article for more advanced examples and explanations.

Examples


s <- stratify(by = "bbs_cws", sample_data = TRUE)
#> Using 'bbs_cws' (standard) stratification
#> Using sample BBS data...
#> Using species Pacific Wren (sample data)
#> Stratifying data...
#>   Combining BCR 7 and NS and PEI...
#>   Renaming routes...
p <- prepare_data(s)
pm <- prepare_model(p, model = "first_diff", model_variant = "hier")

# Run model (quick and dirty)
m <- run_model(pm, iter_warmup = 20, iter_sampling = 20, chains = 2)
#> One set of `init` values supplied, duplicating for each chain.
#> Running MCMC with 2 chains, at most 4 in parallel...
#> 
#> Chain 1 WARNING: There aren't enough warmup iterations to fit the 
#> Chain 1          three stages of adaptation as currently configured. 
#> Chain 1          Reducing each adaptation stage to 15%/75%/10% of 
#> Chain 1          the given number of warmup iterations: 
#> Chain 1            init_buffer = 3 
#> Chain 1            adapt_window = 15 
#> Chain 1            term_buffer = 2 
#> Chain 1 Iteration:  1 / 40 [  2%]  (Warmup) 
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: neg_binomial_2_log_lpmf: Precision parameter is 0, but must be positive finite! (in '/tmp/RtmpDv9MRA/model-24731b8f724d.stan', line 227, column 3 to column 40)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1 
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: neg_binomial_2_log_lpmf: Precision parameter is 0, but must be positive finite! (in '/tmp/RtmpDv9MRA/model-24731b8f724d.stan', line 227, column 3 to column 40)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1 
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: neg_binomial_2_log_lpmf: Precision parameter is 0, but must be positive finite! (in '/tmp/RtmpDv9MRA/model-24731b8f724d.stan', line 227, column 3 to column 40)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1 
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: neg_binomial_2_log_lpmf: Precision parameter is inf, but must be positive finite! (in '/tmp/RtmpDv9MRA/model-24731b8f724d.stan', line 227, column 3 to column 40)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1 
#> Chain 2 WARNING: There aren't enough warmup iterations to fit the 
#> Chain 2          three stages of adaptation as currently configured. 
#> Chain 2          Reducing each adaptation stage to 15%/75%/10% of 
#> Chain 2          the given number of warmup iterations: 
#> Chain 2            init_buffer = 3 
#> Chain 2            adapt_window = 15 
#> Chain 2            term_buffer = 2 
#> Chain 2 Iteration:  1 / 40 [  2%]  (Warmup) 
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: neg_binomial_2_log_lpmf: Precision parameter is 0, but must be positive finite! (in '/tmp/RtmpDv9MRA/model-24731b8f724d.stan', line 227, column 3 to column 40)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2 
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: neg_binomial_2_log_lpmf: Precision parameter is 0, but must be positive finite! (in '/tmp/RtmpDv9MRA/model-24731b8f724d.stan', line 227, column 3 to column 40)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2 
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: neg_binomial_2_log_lpmf: Precision parameter is 0, but must be positive finite! (in '/tmp/RtmpDv9MRA/model-24731b8f724d.stan', line 227, column 3 to column 40)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2 
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: neg_binomial_2_log_lpmf: Precision parameter is inf, but must be positive finite! (in '/tmp/RtmpDv9MRA/model-24731b8f724d.stan', line 227, column 3 to column 40)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2 
#> Chain 1 Iteration: 21 / 40 [ 52%]  (Sampling) 
#> Chain 2 Iteration: 21 / 40 [ 52%]  (Sampling) 
#> Chain 1 Iteration: 40 / 40 [100%]  (Sampling) 
#> Chain 1 finished in 136.0 seconds.
#> Chain 2 Iteration: 40 / 40 [100%]  (Sampling) 
#> Chain 2 finished in 145.1 seconds.
#> 
#> Both chains finished successfully.
#> Mean chain execution time: 140.6 seconds.
#> Total execution time: 145.3 seconds.
#> 
#> Saving model output to /home/runner/work/bbsBayes/bbsBayes/docs/reference/BBS_STAN_first_diff_hier_2023-01-17.rds

# Clean up (remove model files)
unlink(list.files(pattern = paste0("BBS_STAN_first_diff_hier_", Sys.Date())))