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Wrapper for model estimation based on data_prep flag

est_model.Rd

this is a wrapper function around compiling and sampling from the Bayesian bomb radiocarbon model sets. Flags generated in the data_prep function are used to compile the appropriate model specific to your machine's C++ compiler and then sample from the model.

Usage

est_model(data, save_dir, ...)

Arguments

data

data prepared using the data_prep function

save_dir

directory to save cmdstan outputs

...

arguments to 'cmdstanr' sample function

Value

A list containing the STAN model and samples if fitted successfully.

  • model: the CmdStanModel object used to sample from

  • fitted: the returned samples from calling sample on the CmdStanModel object

Examples

#REFERENCE-ONLY
df <- data_prep(sim_ref)
#> [1] "B-spline model indicated, adding additional data values for spline control"
#> No values provided in df_unk, skipping validation and estimating reference series only

#estimate model
fit <- est_model(df, show_messages = FALSE, show_exceptions = FALSE)

#estimate model with args to cmdstanr sample function
if (FALSE) { # \dontrun{
fit <- est_model(df, iter_warmup = 3000, iter_sampling = 500, parallel_chains = 4)
} # }

#INTEGRATED MODEL
df_int <- data_prep(sim_ref, sim_unk)
#> [1] "B-spline model indicated, adding additional data values for spline control"

#estimate model
if (FALSE) { # \dontrun{
fit_int <- est_model(df_int, parallel_chains = 4, iter_warmup=3000, iter_sampling = 250)
} # }

#FIXED NUMBER OF KNOTS
if (FALSE) { # \dontrun{
df <- data_prep(sim_ref, fixed.knot = 12L)
fit <- est_model(df, parallel_chains = 4, iter_warmup=1000, iter_sampling = 250)
} # }