make_time_sequence_data {eyetrackingR}

make_time_sequence_data()

Description

Creates time-bins and summarizes proportion-looking within each time-bin.

Usage

make_time_sequence_data(
  data,
  time_bin_size,
  aois = NULL,
  predictor_columns = NULL,
  other_dv_columns = NULL,
  summarize_by = NULL
)

Arguments

data

The output of make_eyetrackingr_data

time_bin_size

How large should each time bin be? Units are whatever units your time column is in

aois

Which AOI(s) is/are of interest? Defaults to all specified in make_eyetracking_r_data

predictor_columns

Which columns indicate predictor variables, and therefore should be preserved in grouping operations?

other_dv_columns

Within each time-bin, 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 (t.test, ANOVA), while the latter is preferable for mixed-effects models (lmer)

Details

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 arcsine-root transformation of the raw proportions, defined as asin(sqrt(Prop))

• ot - These columns (ot1-ot7) represent (centered) orthogonal time polynomials, needed for growth curve analysis. See the vignette on growth curve models for more details.

Value

Data binned into time-bins, with proportion-looking and transformations as well as orthogonal time-polynomials for growth curve analysis

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
)

# 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"
)