Takes data whose time bins have been clustered by test-statistic (using the make_time_cluster_data function) and performs a permutation test (Maris & Oostenveld, 2007). This analysis takes a summed statistic for each cluster, and compares it to the "null" distribution of sum statistics obtained by shuffling/resampling the data and extracting the largest cluster from each resample.

analyze_time_clusters(data, ...)

# S3 method for time_cluster_data
analyze_time_clusters(
data,
within_subj,
samples = 2000,
formula = NULL,
shuffle_by = NULL,
parallel = FALSE,
quiet = FALSE,
...
)

## Arguments

data

The output of the make_time_cluster_data function

...

Other args for to selected 'test' function; should be identical to those passed to make_time_cluster_data function

within_subj

Logical indicating whether to perform within-subjects bootstrap resampling.

samples

How many iterations should be performed in the bootstrap resampling procedure?

formula

Formula for test. Should be identical to that passed to make_time_cluster_data fxn (if arg ignored there, can be ignored here)

shuffle_by

Along which attribute should the data be resampled? Default is the predictor column. But if the predictor_column is numeric *and* within-subjects, then observations with the same predictor value could nevertheless correspond to distinct conditions/categories that should be shuffled separately. For example, when using vocabulary scores to predict looking behavior, a participant might get identical vocab scores for verbs and nouns; these are nevertheless distinct categories that should be re-assigned separately when bootstrap-resampling data. The 'shuffle_by' argument allows you to specify a column which indicates these kinds of distinct categories that should be resampled separately-- but it's only needed if you've specified a numeric *and* within-subjects predictor column.

parallel

Use foreach for speed boost? By default off. May not work on Windows.

quiet

Display progress bar/messages? No progress bar when parallel=TRUE.

## Value

A cluster-analysis object, which can be plotted and summarized to examine which temporal periods show a significant effect of the predictor variable

## Methods (by class)

• time_cluster_data:

## 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)
response_time <- make_time_sequence_data(response_window, time_bin_size = 500, aois = "Animate",
predictor_columns = "Sex")

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))
summary(time_cluster_data)
plot(time_cluster_data)

# analyze time clusters in a non-parametric analysis

tc_analysis <- analyze_time_clusters(time_cluster_data, within_subj = FALSE,
samples = 2000)
plot(tc_analysis)
summary(tc_analysis)
}