While the run_log(), summary_log(), and
config_log() are helpful for summarizing the model
development process, model_tree() provides a convenient way
to visualize and track any of these tabulated parameters. You can create
a tree diagram using a modeling directory or run log.
MODEL_DIR <- "../nonmem"By default, models will be colored by their run number, and basic summary statistics will display as a tooltip when hovered over.
model_tree(MODEL_DIR)If coloring by a logical column, FALSE and
TRUE values will correspond to white and red coloring
respectively. Numeric or character columns will be colored as a
gradient. NA values will appear grey regardless of the
column type.
model_tree(MODEL_DIR, color_by = "star")Specific columns can be added to the tooltip via the
include_info argument. Though you are limited to the
default run_log() columns when passing a model directory,
you can pass any available columns when passing a run log dataframe
(must inherit the class bbi_log_df). The examples below to
illustrate cases where you may want to do that.
log_df <- run_log(MODEL_DIR)
log_df
#> # A tibble: 9 × 10
#> absolute_model_path run yaml_md5 model_type description bbi_args
#> <chr> <chr> <chr> <chr> <chr> <list>
#> 1 /data/home/barrettk/.cache… 1 6ccf206… nonmem original a… <named list>
#> 2 /data/home/barrettk/.cache… 2 b5f8010… nonmem NA <named list>
#> 3 /data/home/barrettk/.cache… 3 99d902b… nonmem NA <named list>
#> 4 /data/home/barrettk/.cache… 4 5161777… nonmem NA <named list>
#> 5 /data/home/barrettk/.cache… 5 690952d… nonmem NA <named list>
#> 6 /data/home/barrettk/.cache… 6-bo… 9d66680… nmboot NA <named list>
#> 7 /data/home/barrettk/.cache… 6 324dd95… nonmem final model <named list>
#> 8 /data/home/barrettk/.cache… 7 661f9f1… nonmem NA <named list>
#> 9 /data/home/barrettk/.cache… 8 994cb9f… nonmem NA <named list>
#> # ℹ 4 more variables: based_on <list>, tags <list>, notes <list>, star <lgl>In this example we define a new column, out_of_date, to
denote whether the model or data has changed since the last run. We can
color by this new column to determine if any of the models need to be
re-run:
run_log(MODEL_DIR) %>%
add_config() %>%
dplyr::mutate(out_of_date = model_has_changed | data_has_changed) %>%
model_tree(
include_info = c("model_has_changed", "data_has_changed", "nm_version"),
color_by = "out_of_date"
)The model tree can also be helpful for quickly determine if any heuristics were found during any model submissions, as well as displaying specific model summary output in the tooltip.
run_log(MODEL_DIR) %>%
add_summary() %>%
model_tree(
include_info = c("tags", "param_count", "eta_pval_significant"),
color_by = "any_heuristics"
)