Bootstrap resample splines for time-series data. — make_boot_splines

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)

make_boot_splines_data(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") 
}