Creates timebins and summarizes proportionlooking within each timebin.
1 2 
data 
The output of 
time_bin_size 
How large should each time bin be? Units are whatever units your 
aois 
Which AOI(s) is/are of interest? Defaults to all specified in

predictor_columns 
Which columns indicate predictor variables, and therefore should be preserved in grouping operations? 
other_dv_columns 
Within each timebin, this function will calculate not only proportion looking, but also the mean of any columns specified here. 
summarize_by 
Should the data be summarized along, e.g., participants, items, etc.? If
so, give column name(s) here. If left blank, will leave trials distinct. The former is needed
for more traditional analyses ( 
Aside from proportion looking (Prop
), this function returns several columns useful for subsequent
analysis:
LogitAdjusted
 The logit is defined as log( Prop / (1  Prop) )
. This
transformation attempts to map bounded 0,1
data to the real number line. Unfortunately,
for data that is exactly 0 or 1, this is undefined. One solution is add a very small value to
any datapoints that equal 0, and subtract a small value to any datapoints that equal 1 (we use
1/2 the smallest nonzero value for this adjustment).
Elog
 Another way of calculating a corrected logit transformation is to
add a small value epsilon
to both the numerator and denominator of the logit equation (we
use 0.5).
Weights
 These attempt to further correct the Elog transformation, since the
variance of the logit depends on the mean. They can be used in a mixed effects model by setting
the weights=Weights
in lmer
(note that this is the reciprocal of the
weights calculated in this empirical logit
walkthrough, so you do *not* set weights = 1/Weights
as done there.)
ArcSin
 The arcsineroot transformation of the raw proportions, defined as
asin(sqrt(Prop))
ot
 These columns (ot1ot7) represent (centered) orthogonal time polynomials,
needed for growth curve analysis. See
the vignette on growth curve
models for more details.
Data binned into timebins, with proportionlooking and transformations as well as orthogonal timepolynomials for growth curve analysis
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30  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
)
# bin data in 250ms bins, and generate a dataframe
# with a single AOI (Animate) predicted by Sex, and summarized by ParticipantName
response_time < make_time_sequence_data(data,
time_bin_size = 250,
predictor_columns = c("Sex"),
aois = "Animate",
summarize_by = "ParticipantName"
)
# optionally specify other columns in the data
# to be included in the generated dataframe
# (e.g., for use in statistical models)
# bin data in 250ms bins, and generate a dataframe
# with Animate and MCDI_Total summarized by ParticipantName
response_time < make_time_sequence_data(data,
time_bin_size = 250,
predictor_columns = c("Sex","MCDI_Total"),
aois = "Animate",
summarize_by = "ParticipantName"
)

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