Two rules kept out of the exported roster. A function on the package index
reads as a recommendation, and neither is one. The code, its tests, and
this page stay so that the rules' behaviour and failure modes can be
inspected. Neither is part of the supported interface, so either can
change without notice. Both return a rule object that rt_screen()
applies like any other.
Arguments
- q_cut
Lower quantile of the tentative cut, in (0, 0.5). The validation reference quantile is
2 * q_cut.- s_accept
Minimum proportional shift of the surviving minimum toward the reference quantile for the tentative cut to be accepted.
- c_ndt
Multiplier on the fitted non-decision time that sets the support bound, in (0, 1]: no valid response time can undercut non-decision time, so nothing above 1 has a grounding.
- refit
Whether to refit the EZ model once on the survivors and re-flag against the updated non-decision time. Exactly one refit; the rule never iterates to convergence.
Value
An object of class c("rtprep_rule_<name>", "rtprep_rule"), as
for the exported constructors in rules.
Adaptive leading-edge trim
rule_adaptive_trim() cuts at a lower quantile and keeps the cut only if
it looks like removed contaminants rather than removed edge. It turns an
unconditional lower trim into a validated one: cut at
the empirical q_cut quantile, then measure how far the surviving minimum
shifted toward the reference quantile at 2 * q_cut,
$$S = \frac{\min(kept) - \min(all)}{q_{2 q_{cut}}(all) - \min(all)},$$
and keep the cut only when S >= s_accept. Displaced fast contaminants sit
in a low block with a gap to the core, so removing them jumps the minimum
most of the way to the reference (S near 1); a genuinely steep leading
edge bunches its fastest trials, so cutting them barely moves the minimum
(S near 0). When the cut is rejected the rule removes nothing, and the
computed S and the decision are reported in attr(x, "fits") either way.
What the statistic actually detects is a gap below the leading edge. Across-trial variability in non-decision time smears a clean edge into exactly such a shallow front, which is the rule's documented false-alarm mode. Groups with fewer than 20 trials, and groups whose reference quantile ties the minimum, are left untouched.
EZ support screen
rule_ez_support() flags trials the fitted model says are impossible. Its
two failure modes follow from that premise: late delayed start-ups drag the
fitted non-decision time below zero and the rule reverts to keeping
everything, while across-trial variability in non-decision time pushes
genuine trials under the bound and the rule removes them. Every
evidence accumulation model writes a response time as
non-decision time plus a strictly positive decision time, so no valid trial
can undercut non-decision time. The rule fits the closed-form EZ model to a
group's trials, flags everything below c_ndt times the fitted
non-decision time, refits once on the survivors (refit = TRUE), re-flags
against the updated estimate, and stops there. It never iterates further,
because lower-tail removal shrinks the variance and pushes the estimate
upward, a one-way ratchet that unlimited iteration would run away with.
The catch is the point: fast contaminants drag the fitted non-decision time
down, so the rule's premise is poisoned by exactly the trials it hunts.
Whether one refit recovers the threshold is an empirical question, not a
guarantee. Groups with fewer than ten trials, unusable fits (including a
negative fitted non-decision time, which contaminated moments can produce),
and fits that would flag more than half the group all remove nothing, with
usable = FALSE in attr(x, "fits"). That last guard is defensive: at
c_ndt <= 1 a first-pass EZ threshold cannot exceed the sample median,
because the mean never sits more than one standard deviation above the
median while the implied decision-time mean always exceeds it.
This rule requires response, coded as correct/error.