Plot time-sequence data — plot.time_sequence

Plot the timecourse of looking. Each AOI will be plotted in a separate pane, and data can be split into groups by a predictor column. Data is collapsed by subject for plotting. Supports overlaying the predictions of a growth-curve mixed effects model on the data

# S3 method for time_sequence_data
plot(x, predictor_column = NULL, dv = "Prop", model = NULL, ...)

Arguments

x: Your data from make_time_sequence_data. Will be collapsed by subject for plotting (unless already collapsed by some other factor).
predictor_column: Data can be grouped by a predictor column (median split is performed if numeric)
dv: What measure of gaze do you want to use? (Prop, Elog, or ArcSin)
model: (Optional) A growth-curve mixed effects model (from lmer) that was used on the time_sequence_data. If model is given, this function will overlay the predictions of that model on the data
...: Ignored

Value

A ggplot object

Examples

if (FALSE) {
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_time <- make_time_sequence_data(data, time_bin_size = 250,
                                         predictor_columns = c("MCDI_Total"),
                                         aois = "Animate", summarize_by = "ParticipantName")

# visualize time results
plot(response_time, predictor_column = "MCDI_Total")
}