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,
...
)

## Arguments

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.

## Value

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.

## Methods (by class)

• time_sequence_data: Make data for time cluster analysis

## 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_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)
)
}