make_time_cluster_data {eyetrackingR}
Make data for cluster analysis.
Description
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.
Usage
make_time_cluster_data(data, ...) ## S3 method for class 'time_sequence_data' make_time_cluster_data(data, predictor_column, aoi = NULL, test, threshold = NULL, formula = NULL, ...)
Arguments
data |
The output of the |
... |
Any other arguments to be passed to the selected 'test' function (e.g., paired, var.equal, etc.) |
predictor_column |
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
|
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 |
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
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) )