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Models in bbsBayes

Source: vignettes/articles/models.Rmd
models.Rmd
library(bbsBayes)
#> bbsBayes v3.0.0
#> Note: version 3+ represents a major shift in functionality, noteably:
#>  - The Bayesian modelling engine has switched from *JAGS* to *Stan*
#>  - The workflow has been streamlined, resulting in deprecated/renamed
#>    function arguments
#> See the documentation for more details: https://BrandonEdwards.github.io/bbsBayes

There are 9 types of models that can be run with bbsBayes. For a quick overview you can access the bbs_models data frame.

bbs_models
#>        model variant                           file
#> 1 first_diff nonhier first_diff_nonhier_bbs_CV.stan
#> 2 first_diff    hier    first_diff_hier_bbs_CV.stan
#> 3 first_diff spatial first_diff_spatial_bbs_CV.stan
#> 4        gam    hier           gam_hier_bbs_CV.stan
#> 5        gam spatial        gam_spatial_bbs_CV.stan
#> 6      gamye    hier         gamye_hier_bbs_CV.stan
#> 7      gamye spatial      gamye_spatial_bbs_CV.stan
#> 8      slope    hier         slope_hier_bbs_CV.stan
#> 9      slope spatial      slope_spatial_bbs_CV.stan
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)

1. First Difference Models (first_diff)

Non-hierarchical

Hierarchical

Spatial

2. General Additive Models (gam)

Hierarchical

Spatial

3. General Additive Models Year Effect(gamye)

Hierarchical

Spatial

4. Slope (slope)

Hierarchical

Spatial

Advanced Modelling

Cross-fold Validation

For cross fold validation, you’ll need to define which observations are part of a test set and which are part of a training set.

To use the method implemented by bbsBayes, we’ll specify calculate_cv as TRUE during the prepare_model() step. By default, this will prepare 10 test sets (K-folds, cv_k) using obs_n as the grouping variable (cv_fold_groups) and omitting groups with only single observations (cv_omit_singles).

MORE DETAILS ON HOW CHOSEN

md <- stratify(by = "bbs_usgs", sample_data = TRUE, quiet = TRUE) %>%
  prepare_data(min_max_route_years = 2) %>%
  prepare_model("first_diff", calculate_cv = TRUE, cv_k = 3) 
#> Note that bbsBayes cross validation is experimental!

Now our model data (md) has a list item called folds which contains the fold groups assigned to each observation. To use this, you’ll need to loop your model and specify the k iteration.

for(k in 1:3) {
  run_model(md, k = k, chains = 2, iter_warmup = 10, iter_sampling = 10)
}
#> One set of `init` values supplied, duplicating for each chain.
#> Running MCMC with 2 chains, at most 4 in parallel...
#> 
#> Chain 1 WARNING: No variance estimation is 
#> Chain 1          performed for num_warmup < 20 
#> Chain 1 Iteration:  1 / 20 [  5%]  (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: Log location parameter[1] is -nan, but must be 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: No variance estimation is 
#> Chain 2          performed for num_warmup < 20 
#> Chain 2 Iteration:  1 / 20 [  5%]  (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: Log location parameter[1] is -nan, but must be 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: 11 / 20 [ 55%]  (Sampling) 
#> Chain 2 Iteration: 11 / 20 [ 55%]  (Sampling) 
#> Chain 1 Iteration: 20 / 20 [100%]  (Sampling) 
#> Chain 1 finished in 22.8 seconds.
#> Chain 2 Iteration: 20 / 20 [100%]  (Sampling) 
#> Chain 2 finished in 24.0 seconds.
#> 
#> Both chains finished successfully.
#> Mean chain execution time: 23.4 seconds.
#> Total execution time: 24.4 seconds.
#> Warning: 1 of 2 chains had an E-BFMI less than 0.2.
#> See https://mc-stan.org/misc/warnings for details.
#> Saving model output to /home/runner/work/bbsBayes/bbsBayes/vignettes/articles/BBS_STAN_first_diff_hier_2023-01-17_k1.rds
#> One set of `init` values supplied, duplicating for each chain.
#> Running MCMC with 2 chains, at most 4 in parallel...
#> 
#> Chain 1 WARNING: No variance estimation is 
#> Chain 1          performed for num_warmup < 20 
#> Chain 1 Iteration:  1 / 20 [  5%]  (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: Log location parameter[1] is -nan, but must be 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: No variance estimation is 
#> Chain 2          performed for num_warmup < 20 
#> Chain 2 Iteration:  1 / 20 [  5%]  (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: Log location parameter[1] is inf, but must be 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: 11 / 20 [ 55%]  (Sampling) 
#> Chain 2 Iteration: 11 / 20 [ 55%]  (Sampling) 
#> Chain 1 Iteration: 20 / 20 [100%]  (Sampling) 
#> Chain 1 finished in 25.5 seconds.
#> Chain 2 Iteration: 20 / 20 [100%]  (Sampling) 
#> Chain 2 finished in 30.0 seconds.
#> 
#> Both chains finished successfully.
#> Mean chain execution time: 27.8 seconds.
#> Total execution time: 30.2 seconds.
#> Warning: 1 of 2 chains had an E-BFMI less than 0.2.
#> See https://mc-stan.org/misc/warnings for details.
#> Saving model output to /home/runner/work/bbsBayes/bbsBayes/vignettes/articles/BBS_STAN_first_diff_hier_2023-01-17_k2.rds
#> One set of `init` values supplied, duplicating for each chain.
#> Running MCMC with 2 chains, at most 4 in parallel...
#> 
#> Chain 1 WARNING: No variance estimation is 
#> Chain 1          performed for num_warmup < 20 
#> Chain 1 Iteration:  1 / 20 [  5%]  (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: Log location parameter[1] is inf, but must be 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: No variance estimation is 
#> Chain 2          performed for num_warmup < 20 
#> Chain 2 Iteration:  1 / 20 [  5%]  (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: Log location parameter[1] is inf, but must be 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 Iteration: 11 / 20 [ 55%]  (Sampling) 
#> Chain 1 Iteration: 11 / 20 [ 55%]  (Sampling) 
#> Chain 2 Iteration: 20 / 20 [100%]  (Sampling) 
#> Chain 2 finished in 26.7 seconds.
#> Chain 1 Iteration: 20 / 20 [100%]  (Sampling) 
#> Chain 1 finished in 33.8 seconds.
#> 
#> Both chains finished successfully.
#> Mean chain execution time: 30.3 seconds.
#> Total execution time: 33.9 seconds.
#> Warning: 1 of 2 chains had an E-BFMI less than 0.2.
#> See https://mc-stan.org/misc/warnings for details.
#> Saving model output to /home/runner/work/bbsBayes/bbsBayes/vignettes/articles/BBS_STAN_first_diff_hier_2023-01-17_k3.rds

AND NOW…

Customizing initial parameter values

To customize the initial parameter values you can create the model data with prepare_model() and then either edit or overwrite the init_values item in the list.

md <- prepare_model(p, model = "first_diff", model_variant = "hier")

Make some changes to the init_values and pass the model_data to run_model().

md[["init_values"]][["sdnoise"]] <- 0.5

m <- run_model(md, 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 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 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 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 1 Iteration: 21 / 40 [ 52%]  (Sampling) 
#> Chain 2 Iteration: 21 / 40 [ 52%]  (Sampling) 
#> Chain 1 Iteration: 40 / 40 [100%]  (Sampling) 
#> Chain 1 finished in 147.7 seconds.
#> Chain 2 Iteration: 40 / 40 [100%]  (Sampling) 
#> Chain 2 finished in 151.3 seconds.
#> 
#> Both chains finished successfully.
#> Mean chain execution time: 149.5 seconds.
#> Total execution time: 151.5 seconds.
#> Saving model output to /home/runner/work/bbsBayes/bbsBayes/vignettes/articles/BBS_STAN_first_diff_hier_2023-01-17.rds

Note that you can supply different init values for each chain. If you supply one set of init values (which is what prepare_model() outputs), run_model(), will duplicate them for each chain for you. If you supply a list of init values such that the length is the same as the number of chains, your input will be used as is.

Customize Stan Model File

Copy model file (and modify by hand in text editor)

new_model <- copy_model_file("first_diff", "hier", tempdir())
#> Copying model file first_diff_hier_bbs_CV.stan to /tmp/RtmpY3yzcR/first_diff_hier_bbs_CV_COPY.stan
new_model
#> [1] "/tmp/RtmpY3yzcR/first_diff_hier_bbs_CV_COPY.stan"

Then give the file path of this new file to prepare_model()

model_data <- prepare_model(p, model = "first_diff", model_variant = "hier",
                            model_file = new_model)