Preprocessing: Pre-processing rules

Description Usage Arguments

Description

These are pre-processing rules that can be used in aat_splithalf, aat_bootstrap, and aat_compute.

Usage

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prune_nothing(ds, ...)

trial_prune_percent_subject(
  ds,
  subjvar,
  rtvar,
  lowerpercent = 0.01,
  upperpercent = 0.99,
  ...
)

trial_prune_percent_sample(
  ds,
  rtvar,
  lowerpercent = 0.01,
  upperpercent = 0.99,
  ...
)

trial_prune_3SD(ds, subjvar, rtvar, ...)

trial_prune_SD_dropcases(
  ds,
  subjvar,
  rtvar,
  trialsd = 3,
  maxoutliers = 0.15,
  ...
)

trial_recode_SD(ds, subjvar, rtvar, trialsd = 3, ...)

case_prune_3SD(ds, ...)

error_replace_blockmeanplus(
  ds,
  subjvar,
  rtvar,
  blockvar,
  errorvar,
  errorbonus,
  ...
)

error_prune_dropcases(ds, subjvar, errorvar, maxerrors = 0.15, ...)

Arguments

ds

A data.frame.

...

Other arguments (ignored).

subjvar

The name of the subject variable.

rtvar

The name of the reaction time variable.

lowerpercent, upperpercent

for trial_prune_percent_subject and trial_prune_percent_sample, the lower and upper proportions beyond which trials are considered outliers and removed (defaults to .01 and .99).

trialsd

The amount of deviation from the participant mean (in SD) after which a trial is considered an outlier and excluded (defaults to 3).

maxoutliers

for trial_prune_SD_dropcases, the maximum percentage of outliers, after which a participant is excluded from the data.

blockvar

The name of the block variable.

errorvar

The name of the error variable.

errorbonus

for error_replace_blockmeanplus, the amount of seconds to add to the block mean and use as a replacement for error trial reaction times (default is 0.6).

maxerrors

for error_prune_dropcases, the maximum percentage of errors, after which a participant is excluded from the data.


AATtools documentation built on July 1, 2020, 8:45 p.m.