plot_caf: Plot conditional accuracy functions

In EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice

Plot conditional accuracy functions

Description

Plots panels of conditional accuracy functions (CAFs, one for each level of caf_factor on the same panel). Accuracy is calculated with smoothing box car filter on percentile ranges, 0..X, 1..(X+1), ... , (100-X+1).. Inf, where 1 < X <= 50. Optionally, posterior and/or prior predictive CAFs can be overlaid.

Usage

plot_caf(
  input,
  post_predict = NULL,
  prior_predict = NULL,
  subject = NULL,
  quants = c(0.025, 0.975),
  functions = NULL,
  factors = NULL,
  caf_factor = NULL,
  n_cores = 1,
  n_post = 50,
  layout = NA,
  to_plot = c("data", "posterior", "prior")[1:2],
  use_lim = c("data", "posterior", "prior")[1:2],
  legendpos = c("bottomleft", "bottomright"),
  posterior_args = list(),
  prior_args = list(),
  accuracy_function = function(d) d$S == d$R,
  smooth_window = 5,
  which_plot = 1:2,
  ...
)

Arguments

input	Either an emc object or a data frame, or a list of such objects.
post_predict	Optional posterior predictive data (matching columns) or list thereof.
prior_predict	Optional prior predictive data (matching columns) or list thereof.
subject	Subset the data to a single subject (by index or name).
quants	Numeric vector of credible interval bounds (e.g. c(0.025, 0.975)).
functions	A function (or list of functions) that create new columns in the datasets or predictives
factors	Character vector of factor names to aggregate over; defaults to plotting full data set ungrouped by factors if NULL.
caf_factor	The name of within-panel factor
n_cores	Number of CPU cores to use if generating predictives from an emc object.
n_post	Number of posterior draws to simulate if needed for predictives.
layout	Numeric vector used in par(mfrow=...); use NA for auto-layout.
to_plot	Character vector: any of "data", "posterior", "prior".
use_lim	Character vector controlling which source(s) define xlim.
legendpos	Character vector controlling the positions of the legends
posterior_args	Optional list of graphical parameters for posterior lines/ribbons.
prior_args	Optional list of graphical parameters for prior lines/ribbons.
accuracy_function	Accuracy score, default: function(d) d$S==d$R,
smooth_window	range of RT over which calculate accuracy, default 5
which_plot	which of levels of caf_factor to plot, default is both i.e,. which_plot = 1:2
...	Other graphical parameters for the real data lines.

Value

Returns NULL invisibly.

Examples

# Plot conditional accuracy function for data only,
# NB: the caf_factor must have two levels levels.
# forstmann_speed_accuracy <- forstmann[forstmann$E!="neutral",]
# forstmann_speed_accuracy$E <- droplevels(forstmann_speed_accuracy$E)
# plot_caf(forstmann_speed_accuracy, caf_factor="E",factors="S", smooth_window=10)
#
# Or a list of multiple emc objects ...

EMC2 documentation built on Dec. 2, 2025, 9:06 a.m.