PlotAPCE: Plot APCE

In aihuman: Experimental Evaluation of Algorithm-Assisted Human Decision-Making

Plot APCE

Description

See Figure 4 for example.

Usage

PlotAPCE(
  res,
  y.max = 0.1,
  decision.labels = c("signature bond", "small cash bond", "large cash bond"),
  shape.values = c(16, 17, 15),
  col.values = c("blue", "black", "red", "brown"),
  label = TRUE,
  r.labels = c("safe", "easily\npreventable", "prevent-\nable", "risky\n"),
  label.position = c("top", "top", "top", "top"),
  top.margin = 0.01,
  bottom.margin = 0.01,
  name.group = c("Overall", "Female", "Male", "Non-white\nMale", "White\nMale"),
  label.size = 4
)

Arguments

res	A data.frame generated with APCEsummary().
y.max	Maximum value of y-axis.
decision.labels	Labels of decisions (D).
shape.values	Shape of point for each decisions.
col.values	Color of point for each principal stratum.
label	A logical argument whether to specify label of each principal stratum. The default is TRUE.
r.labels	Label of each principal stratum.
label.position	The position of labels.
top.margin	Top margin of labels.
bottom.margin	Bottom margin of labels.
name.group	A character vector including the labels of five subgroups.
label.size	Size of label.

Value

A ggplot.

Examples

data(synth)
sample_mcmc <- AiEvalmcmc(data = synth, n.mcmc = 10)
subgroup_synth <- list(
  1:nrow(synth), which(synth$Sex == 0), which(synth$Sex == 1),
  which(synth$Sex == 1 & synth$White == 0), which(synth$Sex == 1 & synth$White == 1)
)
sample_apce <- CalAPCE(
  data = synth, mcmc.re = sample_mcmc,
  subgroup = subgroup_synth
)
sample_apce_summary <- APCEsummary(sample_apce[["APCE.mcmc"]])
PlotAPCE(sample_apce_summary,
  y.max = 0.25, decision.labels = c(
    "signature", "small cash",
    "middle cash", "large cash"
  ), shape.values = c(16, 17, 15, 18),
  col.values = c("blue", "black", "red", "brown", "purple"), label = FALSE
)

aihuman documentation built on April 12, 2025, 1:47 a.m.