Plot median predictions and prediction intervals as pointintervals against observed data

Usage

plot_pred_obs_pointintervals(
  scorable_table,
  interval_widths = c(0.5, 0.95),
  target_date_col = "target_end_date",
  horizon_col = "horizon",
  reference_date_col = "reference_date",
  predicted_col = "predicted",
  observed_col = "observed",
  quantile_level_col = "quantile_level",
  x_label = "Date",
  y_label = "Target",
  y_transform = "log10",
  quantile_tol = 10,
  predicted_point_size = 3,
  predicted_point_shape = 23,
  predicted_point_fill = "lightblue",
  predicted_interval_color = "darkblue",
  observed_point_size = 3,
  observed_point_shape = 21,
  observed_point_fill = "darkred",
  observed_linetype = "solid",
  observed_linewidth = 1,
  observed_linecolor = "black"
)

Arguments

scorable_table: quantile table with observations, as the output of scoringutils::as_forecast_quantile(), or as a table coercible to a scoringutils-ready quantile forecast via scoringutils::as_forecast_quantile(). Must contain the median (0.5) quantile and the endpoint quantiles for the equal-tailed prediction interval specified in prediction_interval_width. Must contain a column specifying the forecast horizon. Plot will be faceted into rows and columns, with rows representing different horizons and columns representing all other forecast unit variables beside horizon and target date.
interval_widths: Width(s) of the equal-tailed prediction interval(s) to plot around the median. Must correspond to quantiles available in scorable_table. Default c(0.5, 0.95).
target_date_col: Name of the column in scorable_table giving the forecast target date for a given prediction. Default "target_end_date" (as in hubverse schema).
horizon_col: Name of the column in scorable_table containing the forecast horizon for a given row. Default "horizon" (as in hubverse schema).
reference_date_col: Name of the column in scorable_table containing the forecast "reference date": the target date corresponding to the 0 horizon. Silently dropped if present because it is redundant with horizon_col. Default "reference_date" (as in hubverse schema).
predicted_col: Name of the column in scorable_table giving the predicted values. Default "predicted" (as in the output of scoringutils::as_forecast_quantile(). Passed as the predicted argument to scoringutils::as_forecast_quantile().
observed_col: Name of the column in scorable_table giving the observed values. Default "observed" (as in the output of scoringutils::as_forecast_quantile()). Passed as the observed argument to scoringutils::as_forecast_quantile().
quantile_level_col: Name of the column in scorable_table giving the quantile level for a given row. Default "quantile_level" (as in the output of scoringutils::as_forecast_quantile()). Passed as the quantile_level argument to scoringutils::as_forecast_quantile().
x_label: Label for the x axis in the plot. Default "Date".
y_label: Label for the y axis in the plot. Default "Target".
y_transform: Transformation for the y axis in the plot. Passed as the transform argument to ggplot2::scale_y_continuous(). Default "log10".
quantile_tol: Round quantile level values to this many decimal places, to avoid problems with floating point number equality comparisons. Affects both the target quantile level values determined from prediction_interval_width and the quantile level values in the quantile_level_col column of scorable_table. Passed as the digits argument to base::round(). Default 10.
predicted_point_size: Size for the points showing median predictions. Passed as the point_size argument to ggdist::geom_pointinterval(). Default 3.
predicted_point_shape: Shape for the points showing median predictions. Passed as the shape argument to ggdist::geom_pointinterval(). Default 23 (filled diamonds).
predicted_point_fill: Fill color for the points showing median predictions. Passed as the point_fill argument to ggdist::geom_pointinterval(). Default "lightblue".
predicted_interval_color: Color for the lines showing predictions intervals. Passed as the interval_color argument to ggdist::geom_pointinterval() Default "darkblue".
observed_point_size: Size for the points showing observed values. Passed as the size argument to geom_line_point(). Default 3.
observed_point_shape: Shape for the points showing observed values. Passed as the shape argument to geom_line_point(). Default 21 (filled circles).
observed_point_fill: Fill color for the points showing observed values. Passed as the fill argument to geom_line_point(). Default "darkred".
observed_linetype: Type of line to connect the timeseries of observed values. Passed as the "linetype" argument to geom_line_point(). Default "solid".
observed_linewidth: Width for the line connecting the timeseries of observed values. Passed as the "linewidth" argument to geom_line_point(). Default 1.
observed_linecolor: Color for the line connecting the timeseries of observed values. Passed as the "color" argument to geom_line_point(). Default "black".

Value

The plot, as a ggplot2::ggplot() object.

Examples

scoringutils::example_quantile |>
  dplyr::filter(
    location == "IT",
    target_type == "Cases",
    model == "EuroCOVIDhub-ensemble"
  ) |>
  plot_pred_obs_pointintervals()


scoringutils::example_quantile |>
  dplyr::filter(
    location == "IT",
    target_type == "Cases",
    model %in% c("EuroCOVIDhub-ensemble", "EuroCOVIDhub-baseline")
  ) |>
  plot_pred_obs_pointintervals()
#> Warning: log-10 transformation introduced infinite values.