make_boot_splines_data: Bootstrap resample splines for time-series data.

In eyetrackingR: Eye-Tracking Data Analysis

Bootstrap resample splines for time-series data.

Description

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

Usage

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

## S3 method for class '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

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.

Value

A bootstrapped distribution of samples for each time-bin

Methods (by class)

• make_boot_splines_data(time_sequence_data):

Examples

## Not run: 
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") 

## End(Not run)

eyetrackingR documentation built on Sept. 15, 2023, 5:08 p.m.