sdt_cdp_logmu: CDP category log-probability (multinomial logit)

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

CDP category log-probability (multinomial logit)

Description

R companion to the Stan sdt_cdp_logmu function. It returns log(p_cat) for response category cat (unnormalized, exactly like the Stan function – the category probabilities sum to 1 analytically and softmax absorbs the shared constant), which the sdt_cdp() multinomial formula uses as the category logit. brms evaluates the non-linear formula in R for posterior_predict() and posterior_epred(), so this function must be on the search path; it is exported for that reason and is not called directly. Vectorized over the draws-by-observations matrices brms supplies.

Usage

sdt_cdp_logmu(
  cat,
  n_new,
  n_old,
  thresh,
  has_guess,
  dfam,
  drec,
  criterion,
  spacing,
  rcrit,
  sigmar,
  rho,
  kcrit,
  stimulus,
  ...
)

Arguments

cat	Integer response-category index (canonical order: new, then guess/know/remember blocks).
n_new, n_old	Integer numbers of "new" and "old" confidence levels.
thresh	Integer threshold-parameterization id (1 parsimonious, 2 equidistant, 3 log_distance).
has_guess	Integer flag (1 if the Know/Guess split is active).
dfam, drec, criterion, spacing, rcrit, sigmar, rho, kcrit	Model parameters (draws-by-observation matrices supplied by brms). kcrit is ignored when has_guess is 0. spacing is the fixed literal 0 for log_distance, which uses the per-distance deltas in ... instead.
stimulus	Stimulus covariate (0 = new/lure, 1 = old/target).
...	For log_distance, the per-distance threshold parameters (delta1, ...) supplied by brms in contiguous order; otherwise unused.

Value

log(p_cat), matching the shape of dfam.

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