Observed ROC points from a fitted SDT model

Computes empirical (observed) ROC points from the response-count data used to fit a sdt_rating() or sdt_yn() model. Rating models pool counts across observations within each stimulus type (and optional condition) to produce cumulative hit/false-alarm rates at each confidence threshold; binary models produce one operating point per criterion level (the empirical counterpart of the model-implied points from roc_sdt()).

Usage

roc_observed(fit, conditions = NULL)

Arguments

fit

A bmmfit object from a rating or binary SDT model.

conditions

Optional character vector of column names to condition on. For rating models, NULL pools all observations into one ROC. For binary models, NULL auto-detects the criterion-varying predictor(s).

Value

A data frame of class "bmm_sdt_roc_observed" with columns FA, Hit, and any condition columns. Rating models additionally include the (0,0) and (1,1) endpoints. The attribute model_type is "rating" or "binary"; rating results also carry n_ratings.

See also

roc_sdt(), plot.bmm_sdt_roc()

Examples

if (FALSE) { # \dontrun{
# Three base-rate conditions shift the criterion, which identifies sdratio
dat <- expand.grid(id = 1:20, stimulus = c(0L, 1L),
                   condition = c("liberal", "neutral", "strict"))
dat$n_trials <- 100L
# simulated as bias, measured from the midpoint between the means (0 = boundary at the midpoint)
criteria <- c(liberal = -0.5, neutral = 0, strict = 0.5)
dat$n_old <- rsdt_yn(nrow(dat), dat$n_trials, dat$stimulus, d = 1.5,
                     bias = criteria[as.character(dat$condition)],
                     sdratio = 1.3)

fit <- bmm(
  formula = bmf(d ~ 1, criterion ~ 0 + condition, sdratio ~ 1),
  data = dat,
  model = sdt_yn(response = "n_old", stimulus = "stimulus",
                 n_trials = "n_trials"),
  cores = 4,
  backend = "cmdstanr"
)

# One point per base-rate condition, from the response counts
obs <- roc_observed(fit)
obs
plot(roc_sdt(fit), observed = obs)
} # }