Takes data that has been summarized into time-bins by `make_time_sequence_data()`

, finds adjacent time
bins that pass some test-statistic threshold, and assigns these adjacent bins into groups (clusters).
Output is ready for a cluster permutation-based analyses (Maris & Oostenveld, 2007). Supports `t.test`

,
`wilcox.test`

, `(g)lm`

, and `(g)lmer`

. Also includes support for
the "bootstrapped-splines" test (see `?make_boot_splines_data`

and
the divergence vignette for more info).
By default, this function uses 'proportion-looking' (`Prop`

) as the DV, which can be changed
by manually specifying the formula.

```
make_time_cluster_data(data, ...)
# S3 method for time_sequence_data
make_time_cluster_data(
data,
predictor_column,
aoi = NULL,
test,
threshold = NULL,
formula = NULL,
treatment_level = NULL,
...
)
```

- data
The output of the

`make_time_sequence_data`

function- ...
Any other arguments to be passed to the selected 'test' function (e.g., paired, var.equal, etc.)

- predictor_column
The column name containing the variable whose test statistic you are interested in.

- aoi
Which AOI should be analyzed? If not specified (and dataframe has multiple AOIs), then AOI should be a predictor/covariate in your model (so `formula` needs to be specified).

- test
What type of test should be performed in each time bin? Supports

`t.test`

,`(g)lm`

, or`(g)lmer`

. Also includes experimental support for the "bootstrapped-splines" test (see`?make_boot_splines_data`

and the divergence vignette for more info). Does not support`wilcox.test`

.- threshold
Time-bins with test-statistics greater than this amount will be grouped into clusters.

- formula
What formula should be used for test? Optional (for all but

`(g)lmer`

), if unset uses`Prop ~ [predictor_column]`

- treatment_level
If your predictor is a factor, regression functions like `lm` and `lmer` by default will treatment-code it. One option is to sum-code this predictor yourself before entering it into this function. Another is to use the `treatment_level` argument, which specifies the level of the predictor. For example, you are testing a model where `Target` is a predictor, which has two levels, 'Animate' and 'Inanimate'. R will code 'Animate' as the reference level, and code 'Inanimate' as the treatment level. You'd therefore want to set `treatment_level = Inanimate`.

The original data, augmented with information about clusters. Calling summary on this data will
describe these clusters. The dataset is ready for the `analyze_time_clusters`

method.

`make_time_cluster_data(time_sequence_data)`

: Make data for time cluster analysis

```
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_window <- subset_by_window(data, window_start_time = 15500, window_end_time = 21000,
rezero = FALSE)
# identify clusters in the sequence data using a t-test with
# threshold t-value of 2
# (note: t-tests require a summarized dataset)
response_time <- make_time_sequence_data(response_window, time_bin_size = 500, aois = "Animate",
predictor_columns = "Sex",
summarize_by = "ParticipantName")
time_cluster_data <- make_time_cluster_data(data = response_time,
predictor_column = "Sex",
aoi = "Animate",
test = "t.test",
threshold = 2
)
# identify clusters in the sequence data using an lmer() random-effects
# model with a threshold t-value of 1.5.
# random-effects models don't require us to summarize
response_time <- make_time_sequence_data(response_window, time_bin_size = 500, aois = "Animate",
predictor_columns = "Sex")
# but they do require a formula to be specified
time_cluster_data <- make_time_cluster_data(data = response_time,
predictor_column = "SexM",
aoi = "Animate",
test = "lmer",
threshold = 1.5,
formula = LogitAdjusted ~ Sex + (1|Trial) + (1|ParticipantName)
)
}
```