robyn_outputs: Evaluate Models and Output Results into Local Files
In Robyn: Semi-Automated Marketing Mix Modeling (MMM) from Meta Marketing Science

robyn_outputs

R Documentation

Evaluate Models and Output Results into Local Files

Description

Pack robyn_plots(), robyn_csv(), and robyn_clusters() outcomes on robyn_run() results. When UI=TRUE, enriched OutputModels results with additional plots and objects.

Create a plot to visualize the convergence for each of the datasets when running robyn_run(), especially useful for when using ts_validation. As a reference, the closer the test and validation convergence points are, the better, given the time-series wasn't overfitted.

Usage

robyn_outputs(
  InputCollect,
  OutputModels,
  pareto_fronts = "auto",
  calibration_constraint = 0.1,
  plot_folder = NULL,
  plot_folder_sub = NULL,
  plot_pareto = TRUE,
  csv_out = "pareto",
  clusters = TRUE,
  select_model = "clusters",
  ui = FALSE,
  export = TRUE,
  all_sol_json = FALSE,
  quiet = FALSE,
  refresh = FALSE,
  ...
)

## S3 method for class 'robyn_outputs'
print(x, ...)

robyn_csv(
  InputCollect,
  OutputCollect,
  csv_out = NULL,
  export = TRUE,
  calibrated = FALSE
)

robyn_plots(
  InputCollect,
  OutputCollect,
  export = TRUE,
  plot_folder = OutputCollect$plot_folder,
  ...
)

robyn_onepagers(
  InputCollect,
  OutputCollect,
  select_model = NULL,
  quiet = FALSE,
  export = TRUE,
  plot_folder = OutputCollect$plot_folder,
  baseline_level = 0,
  ...
)

ts_validation(OutputModels, quiet = FALSE, ...)

decomp_plot(
  InputCollect,
  OutputCollect,
  solID = NULL,
  exclude = NULL,
  baseline_level = 0
)

Arguments

`InputCollect`, `OutputModels`	`robyn_inputs()` and `robyn_run()` outcomes.
`pareto_fronts`	Integer. Number of Pareto fronts for the output. `pareto_fronts = 1` returns the best models trading off `NRMSE` & `DECOMP.RSSD`. Increase `pareto_fronts` to get more model choices. `pareto_fronts = "auto"` selects the min fronts that include at least 100 candidates. To customize this threshold, set value with `min_candidates`.
`calibration_constraint`	Numeric. Default to 0.1 and allows 0.01-0.1. When calibrating, 0.1 means top 10 selection. Lower `calibration_constraint` increases calibration accuracy.
`plot_folder`	Character. Path for saving plots and files. Default to `robyn_object` and saves plot in the same directory as `robyn_object`.
`plot_folder_sub`	Character. Sub path for saving plots. Will overwrite the default path with timestamp or, for refresh and allocator, simply overwrite files.
`plot_pareto`	Boolean. Set to `FALSE` to deactivate plotting and saving model one-pagers. Used when testing models.
`csv_out`	Character. Accepts "pareto" or "all". Default to "pareto". Set to "all" will output all iterations as csv. Set NULL to skip exports into CSVs.
`clusters`	Boolean. Apply `robyn_clusters()` to output models?
`select_model`	Character vector. Which models (by `solID`) do you wish to plot the one-pagers and export? Default will take top `robyn_clusters()` results.
`ui`	Boolean. Save additional outputs for UI usage. List outcome.
`export`	Boolean. Export outcomes into local files?
`all_sol_json`	Logical. Add all pareto solutions to json export?
`quiet`	Boolean. Keep messages off?
`refresh`	Boolean. Refresh mode
`...`	Additional parameters passed to `robyn_clusters()`
`x`	`robyn_outputs()` output.
`OutputCollect`	`robyn_run(..., export = FALSE)` output.
`calibrated`	Logical
`baseline_level`	Integer, from 0 to 5. Aggregate baseline variables, depending on the level of aggregation you need. Default is 0 for no aggregation. 1 for Intercept only. 2 adding trend. 3 adding all prophet decomposition variables. 4. Adding contextual variables. 5 Adding organic variables. Results will be reflected on the waterfall chart.
`solID`	Character vector. Model IDs to plot.
`exclude`	Character vector. Manually exclude variables from plot.