aggregate_sdt_cdp_data: Aggregate long-format Remember/Know data for 'sdt_cdp()'

In bmm: Easy and Accessible Bayesian Measurement Models Using 'brms'

Aggregate long-format Remember/Know data for sdt_cdp()

Description

Reshapes long-format Remember/Know recognition data – one row per trial, or one row per response category with a count column – into the aggregated wide format sdt_cdp() is fit to: one row per cell and one count column per response category, named ⁠new1 ... new<n_new>⁠, ⁠know<k>⁠ / ⁠remember<k>⁠ (and ⁠guess<k>⁠ when the data contain "guess" judgments), with k on the unified confidence scale ⁠n_new + 1 ... n_new + n_old⁠.

Usage

aggregate_sdt_cdp_data(data, judgment, confidence, count = NULL, response = "")

Arguments

data	A data frame in long format. Cells are defined by all columns other than judgment, confidence, and count (e.g. participant, stimulus class, and any condition variables), which are carried over to the output unchanged.
judgment	The name of the column coding the memory judgment, with values "new", "remember", "know", and optionally "guess".
confidence	The name of the column coding confidence on the unified old/new scale (integer, 1 = most confident "new" to K = most confident "old"; "new" judgments occupy the low levels, "old" judgments the high levels).
count	Optional name of a column of response counts. If NULL (default), each row is treated as a single trial. Non-integer counts are rounded with a warning; NA counts and NA confidences are refused.
response	Optional common prefix for the generated count columns (default ""). Pass the same value to the response argument of sdt_cdp().

Value

A data frame with one row per cell: the cell-defining columns followed by the response count columns, ready to pass to bmm() with an sdt_cdp() model. The numbers of "new" and "old" confidence levels are inferred from the data.

Examples

dat <- data.frame(
  stimulus = c(1L, 1L, 1L, 0L, 0L, 0L),
  judgment = c("remember", "know", "new", "new", "know", "new"),
  confidence = c(3L, 2L, 1L, 1L, 3L, 1L),
  count = c(40L, 25L, 15L, 55L, 10L, 20L)
)
aggregate_sdt_cdp_data(dat, "judgment", "confidence", "count")

bmm documentation built on Oct. 16, 2026, 9:08 a.m.