Keep vector from a screening rule

The .keep column of rt_screen() on its own, for the case where a filter is all that is wanted. It reports how many trials it dropped, once per call, so that the exclusion count is logged next to the exclusion rather than reconstructed afterwards.

Usage

rt_keep(
  rt,
  rule,
  response = NULL,
  .by = NULL,
  policy = c("threshold", "probabilistic"),
  threshold = 0.5,
  quiet = FALSE
)

Arguments

rt

Numeric vector of response times in seconds. NA is allowed; non-positive values are an error.

rule

A rule object; see rules.

response

Optional response coding of the same length as rt, given as numeric 0/1, logical, or a character or factor using labels such as "correct"/"error" or "upper"/"lower". Required by rule_ewma() and by rule_mixture() with use_accuracy = TRUE.

.by

Optional grouping of the same length as rt: a vector, factor, list of vectors, or data frame. Rules are fitted separately within each group. NULL treats all trials as one group.

policy

Exclusion policy. "threshold" keeps a trial when its probability of validity exceeds threshold. "probabilistic" keeps it with that probability, drawing once per trial.

threshold

Cut for policy = "threshold", in [0, 1]. Ignored under the probabilistic policy.

quiet

If FALSE (the default), one message states the rule, the number of trials dropped, and the proportion. TRUE suppresses it.

Value

A logical vector the length of rt: TRUE for a trial to keep. Trials with a missing response time or grouping key are FALSE, as in rt_screen().

Details

Inside a grouped filter() the message fires once per group, because the function is called once per group. Pass .by to rt_keep() instead of to filter(): the keep vector is identical either way, and the count then covers the whole data set in one line. Or set quiet = TRUE.

See also

rt_screen() for the probability and the reason alongside the decision; screen_fits() for the per-group diagnostics.

Examples

rt <- c(0.12, 0.31, 0.35, 0.38, 0.42, 0.47, 0.55, 2.90)
keep <- rt_keep(rt, rule_cutoff(0.18, 2.5))
#> cutoff(0.18, 2.5): dropped 2 of 8 trials (25.0%)
rt[keep]
#> [1] 0.31 0.35 0.38 0.42 0.47 0.55

# with dplyr: dat |> filter(rt_keep(rt, rule_sd(2.5), .by = id))