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, ...)
```

- 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

A ggplot object

```
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")
}
```