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Plot conditional accuracy functions

Source: R/plot_data.R
plot_caf.Rd

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 ...