Plot Posterior Predictive Densities Versus Observed Data

Description

This function draws kernel-density curves for posterior-predictive samples and observed data using ggplot2::geom_line(). Each predictive sample’s density is plotted in light blue; the observed density is overlaid in black.

plot_ppd(
  ppd,
  xlab = "Outcome",
  ylab = "Density",
  title = "Posterior Predictive Distribution",
  xlim = NULL,
  obs_color = NULL,
  ppd_color = NULL
)

Arguments

  • ppd: A data.frame containing posterior-predictive samples (one column per sample) and the column with observed data.
  • xlab: Character: x-axis label. Default "Outcome".
  • ylab: Character: y-axis label. Default "Density".
  • title: Character: plot title. Default "Posterior Predictive Distribution".
  • xlim: Numeric vector of length 2 giving the minimum and maximum x-axis values, e.g. c(0, 25). If NULL (default) the limits are c(0, quantile(observed, 0.95)).
  • obs_color: Color for the observed line density
  • ppd_color: Color for the posterior predictive distribution lines density

Returns

A ggplot2 plot object.

Examples

## Not run:

# Load example posterior predictive distribution from the package: 
ppd_df_file <- system.file("examples", "ppd_df.rds", package = "GHRmodel")
ppd_df <- readRDS(ppd_df_file) # loads ghr_models into the environment

# Plot densities of the posterior predictive distribution and observed cases. 
plot_ppd(ppd_df, 
obs_color = "blue",
ppd_color = "red")
## End(Not run)