Deprecated. Performing this analysis should be done by calling analyze_time_bins(test="boot_splines").

make_boot_splines_data(
  data,
  predictor_column,
  within_subj,
  aoi,
  bs_samples,
  smoother,
  resolution,
  alpha,
  ...
)

# S3 method for time_sequence_data
make_boot_splines_data(
  data,
  predictor_column,
  within_subj,
  aoi = NULL,
  bs_samples = 1000,
  smoother = "smooth.spline",
  resolution = NULL,
  alpha = 0.05,
  ...
)

Arguments

data

The output of time_sequence_data()

predictor_column

What predictor var to split by? Maximum two conditions

within_subj

Are the two conditions within or between subjects?

aoi

Which AOI do you wish to perform the analysis on?

bs_samples

How many iterations to run bootstrap resampling? Default 1000

smoother

Smooth data using "smooth.spline," "loess," or "none" for no smoothing

resolution

What resolution should we return predicted splines at, in ms? e.g., 10ms = 100 intervals per second, or hundredths of a second. Default is the same size as time-bins.

alpha

p-value when the groups are sufficiently "diverged"

...

Ignored

Value

A bootstrapped distribution of samples for each time-bin

Details

This method builds confidence intervals around proportion-looking data by bootstrap resampling. Data can be smoothed by fitting smoothing splines. This function performs the bootstrap resampling, analyze_boot_splines generates confidence intervals and tests for divergences.

Limited to statistical test between two conditions.

Methods (by class)

  • time_sequence_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", 
                                         summarize_by = "ParticipantName")
                                         
df_bootstrapped <- make_boot_splines_data(response_time, 
                                          predictor_column = 'Sex', 
                                          within_subj = FALSE, 
                                          bs_samples = 500, 
                                          alpha = .05,
                                          smoother = "smooth.spline") 
}