A brief example of eyetrackingR

brief-eyetrackingR-example.R

# load *eyetrackingR* and set data options
library(eyetrackingR)

data_options = set_data_options(item_columns = "Trial", trial_column = "Trial", time_column = "TimeFromPhaseOnset", trackloss_column = "TrackLoss", aoi_columns = c('Animate','Inanimate'), participant_column = "ParticipantName")

# verify colums in dataset
master_clean <- verify_dataset(master_clean, data_options)

# analyze and remove trackloss-ridden trials
master_clean <- convert_non_aoi_to_trackloss(master_clean, data_options)
trackloss <- trackloss_analysis(master_clean, data_options, window_start = 15500, window_end = 21000)
master_clean <- clean_by_trackloss(master_clean, data_options, participant_z_thresh = Inf, trial_z_thresh = 2, participant_prop_thresh = 1, trial_prop_thresh = .5, window_start = 15500, window_end = 21000)
master_clean <- remove_trackloss(master_clean, data_options, delete_rows = TRUE)

# zoom in on response window
word_window <- subset_by_window(master_clean, data_options, window_start = 15500, window_end = 21000)

# describe response data in summary
data_summary <- describe_column(word_window, data_options, c('ParticipantName','Condition'))

# perform growth curve analysis
word_time <- time_analysis(word_window, data_options, time_bin_size = 100, condition_columns = "Target", dv = c("Animate"))
plot(word_time, data_options, condition_column = "Target")

word_time$TargetC <- ifelse(word_time$Target == 'Animate', .5, -.5)
word_time$TargetC <- word_time$TargetC - mean(word_time$TargetC)

# use lme4 for mixed-effects modeling
library(lme4)
model <- lmer(Elog ~ TargetC*(ot1 + ot2 + ot3 + ot4 + ot5) + (1 | Trial) + (1 | ParticipantName), data = word_time, REML = FALSE)
summary(model)
drop1(model,~.,test="Chi")