plot_counterfactual: Prepare Plot Data and Plot Counterfactuals

In ubair: Effects of External Conditions on Air Quality

Prepare Plot Data and Plot Counterfactuals

Description

Smooths the predictions using a rolling mean, prepares the data for plotting, and generates the counterfactual plot for the application window. Data before the red box are reference window, red box is buffer and values after black, dotted line are effect window.

Usage

plot_counterfactual(
  predictions,
  params,
  window_size = 14,
  date_effect_start = NULL,
  buffer = 0,
  plot_pred_interval = TRUE
)

Arguments

predictions	The data.table containing the predictions (hourly)
params	Parameters for plotting, including the target variable.
window_size	The window size for the rolling mean (default is 14 days).
date_effect_start	A date. Start date of the effect that is to be evaluated. The data from this point onwards is disregarded for calculating model performance
buffer	Integer. An additional, optional buffer window before date_effect_start to account for uncertainty in the effect start point. Disregards additional buffer data points for model evaluation. Use buffer=0 for no buffer.
plot_pred_interval	Boolean. If TRUE, shows a grey band of the prediction interval.

Details

The optional grey ribbon is a prediction interval for the hourly values. The interpretation for a 90% prediction interval (to be defined in alpha parameter of run_counterfactual()) is that 90% of the true hourly values (not the rolled means) lie within the grey band. This might be helpful for getting an idea of the variance of the data and predictions.

Value

A ggplot object with the counterfactual plot. Can be adjusted further, e.g. set limits for the y-axis for better visualisation.

ubair documentation built on April 12, 2025, 2:12 a.m.