Remove trials/participants with too much trackloss, with a customizable threshold.
clean_by_trackloss(
data,
participant_prop_thresh = 1,
trial_prop_thresh = 1,
window_start_time = -Inf,
window_end_time = Inf
)
Data already run through make_eyetrackingr_data
Maximum proportion of trackloss for participants
Maximum proportion of trackloss for trials
Time-window within which you want trackloss analysis to be based. Allows you to keep the entire trial window for data, but clean based on the trackloss within a subset of it
Cleaned data
data(word_recognition)
data <- make_eyetrackingr_data(word_recognition,
participant_column = "ParticipantName",
trial_column = "Trial",
time_column = "TimeFromTrialOnset",
trackloss_column = "TrackLoss",
aoi_columns = c('Animate','Inanimate'),
treat_non_aoi_looks_as_missing = TRUE
)
# scrub all trials with greater than 25% trackloss, and all
# participants with greater than 25% trackloss on average
# during the timeperiod 15500-2100
data_clean <- clean_by_trackloss(data,
participant_prop_thresh = .25,
trial_prop_thresh = .25,
window_start_time = 15500,
window_end_time = 21000
)
#> Performing Trackloss Analysis...
#> Will exclude trials whose trackloss proportion is greater than : 0.25
#> ...removed 33 trials.
#> Will exclude participants whose trackloss proportion is greater than : 0.25
#> ...removed 3 participants.
# scrub all trials with greater than 25% trackloss, but leave participants with a high average
data_clean <- clean_by_trackloss(data,
trial_prop_thresh = .25,
window_start_time = 15500,
window_end_time = 21000
)
#> Performing Trackloss Analysis...
#> Will exclude trials whose trackloss proportion is greater than : 0.25
#> ...removed 33 trials.