make_switch_data: Summarize data into time-to-switch from initial AOI.
In jwdink/eyetrackingR: Eye-Tracking Data Analysis

Description Usage Arguments Value Methods (by class) Examples

Take trials split by initial-AOI, and determine how quickly participants switch away from that AOI

make_switch_data(data, predictor_columns, summarize_by)

## S3 method for class 'onset_data'
make_switch_data(data, predictor_columns = NULL,
  summarize_by = NULL)

`data`	The output of `make_onset_data`
`predictor_columns`	Variables/covariates of interest when analyzing time-to-switch
`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.tests, ANOVAs), while the latter is preferable for mixed-effects models (lmer)

A dataframe indicating initial AOI and time-to-switch from that AOI for each trial/subject/item/etc.

onset_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
)
response_window <- subset_by_window(data, window_start_time = 15500, window_end_time = 21000, 
                                    rezero = FALSE)
inanimate_trials <- subset(response_window, grepl('(Spoon|Bottle)', Trial))
onsets <- make_onset_data(inanimate_trials, onset_time = 15500, 
                          fixation_window_length = 100, target_aoi='Inanimate')
                          
df_switch <- make_switch_data(onsets, predictor_columns = "MCDI_Total", 
             summarize_by = "ParticipantName")
plot(df_switch, "MCDI_Total")