A composite is a rule, so it goes wherever a rule goes: rt_screen(),
rt_keep(), screen_fits(), screen_compare().
Arguments
- ...
Two or more rule objects.
- threshold
Probability above which a trial survives a stage and is passed to the next. Only
rule_then()takes this: which trials the later stages see is part of the rule, not part of the exclusion policy applied afterwards byrt_screen().
Details
rule_all() keeps a trial only when every component keeps it, so the trials
it removes are the union of what the components remove. rule_any() keeps
a trial that any component keeps, so it removes only the intersection.
rule_then() runs the components in order, each estimated on the trials the
previous ones left.
Which one you want
Screening rules disagree because they look in different places. The location
and spread criteria and the recursive criteria read the slow tail; on
anticipations at the leading edge of a right-skewed distribution they remove
nothing, because those trials sit well inside a criterion built around the
mean. rule_ewma() reads accuracy and finds the leading edge, and is blind
to the slow tail. Combining one of each with rule_all() covers both,
which no single conventional rule does.
rule_then() is the other idiom, and it is what a two-stage description like
"trials below 200 ms were discarded, then trials more than 2.5 SD from each
participant's mean" actually means: the standard deviation is computed after
the floor, on the survivors. trimr::sdTrim(minRT = , sd = ) does exactly
this, and rule_then(rule_cutoff(0.2), rule_sd(2.5)) reproduces it. Writing
the two stages in parallel instead gives a different answer, because the
fast trials are still inflating the standard deviation when it is estimated.
What .prob becomes
rtprep keeps the probability that a trial is valid separate from the
decision to drop it, and a composite has to preserve that. rule_all()
takes the smallest of the component probabilities and rule_any() the
largest, so the composite's .prob is still a probability of validity and
still drives policy = "probabilistic" and rt_summary(weights = ). For
components that return 0 and 1 this reduces to the logical operation, and a
mixture combined with a deterministic rule keeps its posterior wherever the
deterministic rule does not veto.
rule_then() reports the probability from the stage that flagged the trial,
or from the last stage to see it if none did. .reason comes from the first
component to flag, in the order given.
What screen_fits() reports
One row per group, as always, with each component's diagnostics prefixed by
its position: r1_lower, r2_criterion, and so on. rule_then() adds
r1_n_flagged, r2_n_flagged, ... so it is visible how much work each stage
did, which is the number a staged pipeline usually wants and rarely reports.
See also
rules for the components.
Examples
# the slow tail and the leading edge, which no single rule covers
rule_all(rule_mad(2.5), rule_ewma())
#> <rtprep rule> all(sd(2.5, median, mad), ewma(0.01, 1.5))
#> Exclude a trial that any of these rules excludes:
#> 1. sd(2.5, median, mad)
#> 2. ewma(0.01, 1.5)
# a floor, then a criterion estimated on what the floor left
staged <- rule_then(rule_cutoff(0.2), rule_sd(2.5))
staged
#> <rtprep rule> then(cutoff(0.2, Inf), sd(2.5, mean, sd))
#> Apply in order, each estimated on the trials the last one left (stage threshold 0.5):
#> 1. cutoff(0.2, Inf)
#> 2. sd(2.5, mean, sd)
screen_fits(rt_example$rt, staged, .by = rt_example$id)
#> .group n_trials n_dropped prop_dropped r1_lower r1_upper r1_n_seen
#> 1 p1 200 6 0.030 0.2 Inf 200
#> 2 p2 200 6 0.030 0.2 Inf 200
#> 3 p3 200 6 0.030 0.2 Inf 200
#> 4 p4 200 7 0.035 0.2 Inf 200
#> r1_n_flagged r2_center r2_scale r2_lower r2_upper r2_n_seen r2_n_flagged
#> 1 0 0.6029486 0.2324541 0.02181338 1.184084 200 6
#> 2 0 0.6125909 0.2707331 -0.06424181 1.289424 200 6
#> 3 0 0.6327961 0.3409229 -0.21951107 1.485103 200 6
#> 4 0 0.6173370 0.3413135 -0.23594682 1.470621 200 7