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Plot sensitivity analysis heatmaps

plot_sensitivity_analysis_heatmap.Rd

This function generates a heatmap visualization of sensitivity analysis results, showing the impact of varying model parameters on total deaths (or another outcome) for a given model. Positive values represent deaths averted, while negative values represent deaths abetted. The heatmap uses a diverging color scale (red white blue) with qualitative legend labels for interpretability.

Usage

plot_sensitivity_analysis_heatmap(
  result,
  sensitivity_analysis = "oat_ret",
  model_name = "otem",
  breaks = NULL
)

Arguments

result

A list containing sensitivity analysis outputs. Must include an element named according to sensitivity_analysis that contains:

  • data: A data frame with columns model, x_scale, y_scale, and total_deaths.

  • x_label, y_label: Character strings for axis labels.

  • x_tick_labels, y_tick_labels: Optional vectors of tick labels for the x and y axes.

sensitivity_analysis

Character string specifying which sensitivity analysis result to use from result. Defaults to "oat_ret".

model_name

Character string specifying the model to plot within the sensitivity analysis data. Defaults to "otem".

breaks

Numeric vector of length 2 giving the minimum and maximum values for the color scale. If NULL (default), the limits are determined from the observed range of category (i.e., reversed total deaths).

Value

A ggplot object representing the sensitivity analysis heatmap.

Details

The function filters the sensitivity analysis data for the specified model, reverses the sign of total deaths so that positive values indicate deaths averted, and assigns integer indices for the x and y positions. A diverging color gradient is used, centered at zero, with red corresponding to abetted (negative) and blue corresponding to averted (positive) values.

The legend includes qualitative labels ("Abetted -" and "Averted +") along with the numeric range, aiding interpretation of model outcomes.

See also

plot_model_difference_heatmap()

Examples

if (FALSE) { # \dontrun{
# Example usage:
result <- list(
  oat_ret = list(
    data = data.frame(
      model = "otem",
      x_scale = rep(1:3, each = 3),
      y_scale = rep(1:3, 3),
      total_deaths = rnorm(9)
    ),
    x_label = "Parameter A",
    y_label = "Parameter B",
    x_tick_labels = c("Low", "Med", "High"),
    y_tick_labels = c("Low", "Med", "High")
  )
)

plot_sensitivity_analysis_heatmap(result)
} # }