Runs model_summaries() on all models in the input and returns a subset of the each resulting summary as a tibble.
summary_log(.base_dir, .recurse = FALSE, .include = NULL, ...)
add_summary(.log_df, ...)Base directory to look in for models.
If FALSE, the default, only include models in .base_dir
but not subdirectories. This is passed through to fs::dir_ls() --
If TRUE recurse fully, if a positive number the number of levels to recurse.
A character vector specifying which runs or model tags to include in the run log.
Arguments passed through to model_summaries()
a bbi_run_log_df tibble (the output of run_log())
An object of class bbi_summary_log_df, which includes the fields described below. If all model summaries
fail, the returned tibble will only contain the absolute_model_path, run, and error_msg columns.
summary_log() creates a new tibble with one row per model
found in .base_dir (and subdirectories, if .recurse = TRUE).
add_summary() adds these fields to the tibble passed to .log_df.
The following fields from bbi_nonmem_summary (the output of model_summary()) are extracted and included by default.
If you would like more fields from the summary object, you can extract them manually from the bbi_summary list column.
error_msg -- Error message from model_summary(). If NULL the call succeeded. If not NULL, the rest of the fields will be NULL.
needed_fail_flags -- Logical for whether the call initially failed, but passed with the inclusion of .fail_flags. See model_summaries() docs for more details.
bbi_summary -- The full bbi_nonmem_summary object for each row. This can be queried further by extracting it as a list, or by using dplyr::mutate() etc.
ofv -- Objective function value with no constant from the final estimation method. The constant, and the value with the constant can be found in $ofv.
param_count -- Count of (non-fixed) parameters estimated in final estimation method.
estimation_method -- Character vector of estimation method(s) used. Extracted from $run_details.
problem_text -- Character vector of text from $PROB. Extracted from $run_details.
number_of_subjects -- Count of unique subjects in the input data set, extracted from $run_details.
number_of_obs -- Total count of observations in the input data set, extracted from $run_details.
condition_number -- The condition number for the final estimation method, if present.
any_heuristics -- Logical indicating whether any of the columns extracted from $run_heuristics are TRUE. Duplicative information, but helpful for filtering.
$run_heuristics columns -- One logical column for each element extracted from $run_heuristics. These are named and described in the model_summary() docs.