make_time_window_data {eyetrackingR}
Make a dataset collapsing over a timewindow
Description
Collapse time across our entire window and return a dataframe ready for analyses
Usage
make_time_window_data(data, aois = NULL, predictor_columns = NULL, other_dv_columns = NULL, summarize_by = NULL)
Arguments
data 
The output of 
aois 
Which AOI(s) is/are of interest? Defaults to all specified in

predictor_columns 
Which columns indicate predictor vars, and therefore should be preserved in grouping operations? 
other_dv_columns 
Within each participant/trial (or whatever is specified in 
summarize_by 
Should the data be summarized along, e.g., participants, items, etc.? If so, give
column names here. If left blank, will leave trials distinct. The former is needed for more traditional
analyses ( 
Details
Aside from proportion looking (Prop
), this function returns several columns useful for subsequent
analysis:

LogitAdjusted
 The logit is defined aslog( Prop / (1  Prop) )
. This transformation attempts to map bounded0,1
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 valueepsilon
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 theweights=Weights
inlmer
(note that this is the reciprocal of the weights calculated in this empirical logit walkthrough, so you do *not* setweights = 1/Weights
as done there.) 
ArcSin
 The arcsineroot transformation of the raw proportions, defined asasin(sqrt(Prop))
Value
Data with proportionlooking and transformations (logit, arcsin, etc.)
Examples
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 ) # generate a dataset summarizing an AOI (Animate) by ParticipantName response_window_agg_by_sub < make_time_window_data(data, aois='Animate', summarize_by = "ParticipantName" ) # optionally included additional columns for use as predictors # in later statistical models response_window_agg_by_sub < make_time_window_data(data, aois='Animate', predictor_columns=c('Age','MCDI_Total'), summarize_by = "ParticipantName" ) # plot the aggregated data for sanity check plot(response_window_agg_by_sub, predictor_columns="Age", dv = "LogitAdjusted")