Interactive Resampling Accuracy Plots — plot_modeltime

A convenient plotting function for visualizing resampling accuracy by resample set for each model in a Modeltime Table.

Usage

plot_modeltime_resamples(
  .data,
  .metric_set = default_forecast_accuracy_metric_set(),
  .summary_fn = mean,
  ...,
  .facet_ncol = NULL,
  .facet_scales = "free_x",
  .point_show = TRUE,
  .point_size = 1,
  .point_shape = 16,
  .point_alpha = 1,
  .summary_line_show = TRUE,
  .summary_line_size = 0.5,
  .summary_line_type = 1,
  .summary_line_alpha = 1,
  .x_intercept = NULL,
  .x_intercept_color = "red",
  .x_intercept_size = 0.5,
  .legend_show = TRUE,
  .legend_max_width = 40,
  .title = "Resample Accuracy Plot",
  .x_lab = "",
  .y_lab = "",
  .color_lab = "Legend",
  .interactive = TRUE
)

Arguments

.data: A modeltime table that includes a column .resample_results containing the resample results. See modeltime_fit_resamples() for more information.
.metric_set: A yardstick::metric_set() that is used to summarize one or more forecast accuracy (regression) metrics.
.summary_fn: A single summary function that is applied to aggregate the metrics across resample sets. Default: mean.
...: Additional arguments passed to the .summary_fn.
.facet_ncol: Default: NULL. The number of facet columns.
.facet_scales: Default: free_x.
.point_show: Whether or not to show the individual points for each combination of models and metrics. Default: TRUE.
.point_size: Controls the point size. Default: 1.
.point_shape: Controls the point shape. Default: 16.
.point_alpha: Controls the opacity of the points. Default: 1 (full opacity).
.summary_line_show: Whether or not to show the summary lines. Default: TRUE.
.summary_line_size: Controls the summary line width. Default: 0.5.
.summary_line_type: Controls the summary line type. Default: 1.
.summary_line_alpha: Controls the summary line opacity. Default: 1 (full opacity).
.x_intercept: Numeric. Adds an x-intercept at a location (e.g. 0). Default: NULL.
.x_intercept_color: Controls the x-intercept color. Default: "red".
.x_intercept_size: Controls the x-intercept linewidth. Default: 0.5.
.legend_show: Logical. Whether or not to show the legend. Can save space with long model descriptions.
.legend_max_width: Numeric. The width of truncation to apply to the legend text.
.title: Title for the plot
.x_lab: X-axis label for the plot
.y_lab: Y-axis label for the plot
.color_lab: Legend label if a color_var is used.
.interactive: Returns either a static (ggplot2) visualization or an interactive (plotly) visualization

Details

Default Accuracy Metrics

The following accuracy metrics are included by default via modeltime::default_forecast_accuracy_metric_set():

MAE - Mean absolute error, yardstick::mae()
MAPE - Mean absolute percentage error, yardstick::mape()
MASE - Mean absolute scaled error, yardstick::mase()
SMAPE - Symmetric mean absolute percentage error, yardstick::smape()
RMSE - Root mean squared error, yardstick::rmse()
RSQ - R-squared, yardstick::rsq()

Summary Function

Users can supply a single summary function (e.g. mean) to summarize the resample metrics by each model.

Examples


m750_training_resamples_fitted %>%
    plot_modeltime_resamples(
        .interactive = FALSE
    )