Plot Method for pbayesdecisionprob2cont Objects — plot.pbayesdecisionprob2cont • BayesianQDM

Displays operating characteristics for two-continuous-endpoint results returned by pbayesdecisionprob2cont.

Usage

# S3 method for class 'pbayesdecisionprob2cont'
plot(
  x,
  which = "Go",
  title = NULL,
  xlab = NULL,
  ylab = NULL,
  col_go = "#658D1B",
  col_nogo = "#D91E49",
  col_gray = "#939597",
  base_size = 28,
  ...
)

Arguments

x: An object of class pbayesdecisionprob2cont.
which: A character string specifying which decision probability to plot. Must be one of "Go", "Gray", "NoGo", "all", or "overlay". Default is "Go".
title: A character string for the plot title. Defaults to NULL (no title displayed).
xlab: A character string or expression for the x-axis label. Defaults to NULL, which auto-generates a label based on design.
ylab: A character string or expression for the y-axis label. Defaults to NULL, which auto-generates a label based on design.
col_go: A character string specifying the high-end fill colour for the Go probability gradient. Default is "#658D1B".
col_nogo: A character string specifying the high-end fill colour for the NoGo probability gradient. Default is "#D91E49".
col_gray: A character string specifying the high-end fill colour for the Gray probability gradient. Default is "#939597".
base_size: A positive numeric scalar specifying the base font size (in points) passed to theme_bw(). Default is 28.
...: Further arguments passed to or from other methods (ignored).

Value

Invisibly returns a ggplot object (single panel) or a gtable object (which = "all").

Details

When the input scenarios form a regular grid over (mu_t1, mu_t2) (i.e., every combination of the unique values of mu_t1 and mu_t2 is present) and rho_t is constant, the function produces a filled tile plot: each panel (Go, Gray, NoGo) is coloured by its own probability on a continuous gradient (white to the panel colour), so intensity directly reflects the probability magnitude. Otherwise the function falls back to a scatter plot in which point colour encodes the decision probability on a continuous scale.

When which = "all", the three panels are arranged side-by-side using gridExtra::grid.arrange, so each panel retains its own independent colour scale. This requires the gridExtra package.

For design = 'controlled' or design = 'external', both axes are expressed as treatment-minus-control differences: \(\theta_1 = \mu_{t1} - \bar{\mu}_{c1}\) and \(\theta_2 = \mu_{t2} - \bar{\mu}_{c2}\), where \(\bar{\mu}_{c1}\) and \(\bar{\mu}_{c2}\) are the means of the supplied mu_c1 and mu_c2 vectors. For design = 'uncontrolled', the axes represent \(\mu_{t1}\) and \(\mu_{t2}\) directly.

Vertical and horizontal reference lines are drawn at the decision thresholds:

When prob = 'posterior': vertical lines at \(\theta_{TV1}\) and \(\theta_{MAV1}\) (x-axis) and horizontal lines at \(\theta_{TV2}\) and \(\theta_{MAV2}\) (y-axis).
When prob = 'predictive': a single vertical line at \(\theta_{NULL1}\) and a single horizontal line at \(\theta_{NULL2}\).