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Plot Conditional Accuracy Functions (CAFs)

Source: R/plotting.R
plot.cafs.Rd

Visualizes conditional accuracy functions (CAFs) for observed and/or predicted data. This is useful for assessing model fit or exploring response patterns across conditions or participants.

Usage

# S3 method for class 'cafs'
plot(
  x,
  ...,
  id = NULL,
  conds = NULL,
  col = NULL,
  xlim = NULL,
  ylim = c(0, 1),
  xlab = "Bins",
  ylab = NULL,
  interval_obs = TRUE,
  interval_pred = TRUE
)

Arguments

x

an object of type = "cafs", typically returned by calc_stats().

...

additional graphical arguments passed to plotting functions. See set_default_arguments() for the full list of supported options.

id

a numeric or character, specifying the ID of a single participant to plot. If length(id) > 1, plot.cafs() is called recursively for each entry. Each id must match an entry in the ID column of x.

conds

a character vector specifying the conditions to plot. Defaults to all available conditions.

col

a character vector specifying colors for each condition. If a single color is provided, it is repeated for all conditions.

xlim

a numeric vector of length 2, specifying the x-axis limits.

ylim

a numeric vector of length 2, specifying the y-axis limits.

xlab, ylab

character strings for the x- and y-axis labels.

interval_obs, interval_pred

logicals; if TRUE and x contains a column named Estimate, error bars for observed data and shaded contours for predicted data are drawn, respectively.

Value

Returns NULL invisibly. The function is called for its side effect of generating a plot.

Details

If x contains multiple IDs and no specific id is provided, the function aggregates across participants before plotting.

Observed CAFs are shown as points, and predicted CAFs as lines. When interval = TRUE and the input includes interval estimates (i.e., the column Estimate exists), the plot includes error bars for observed data and shaded contours for model predictions.

Colors, symbols, and line styles can be customized via ....

Examples

# Example 1: Model predictions only ---------------------------------------
a_model <- dmc_dm(t_max = 1.5, dx = .01, dt = .005)
cafs <- calc_stats(a_model, type = "cafs")
plot(cafs)

plot(cafs, col = c("green", "red"), ylim = c(0.5, 1))


# Example 2: Observed and predicted data ----------------------------------
obs_data(a_model) <- dmc_synth_data
cafs <- calc_stats(a_model, type = "cafs")
plot(cafs)


# Example 3: Observed data only -------------------------------------------
cafs <- calc_stats(dmc_synth_data, type = "cafs")
plot(cafs)


# Example 4: Observed data with interval ----------------------------------
cafs <- calc_stats(dmc_synth_data, type = "cafs", resample = TRUE)
plot(cafs)