eyetrackingR (version 0.1.8)

analyze_boot_splines: Estimate confidence intervals for bootstrapped splines data

Description

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

Usage

analyze_boot_splines(data)
# S3 method for boot_splines_data
analyze_boot_splines(data)

Arguments

data

The output of the boot_splines_data function

Value

A dataframe indicating means and CIs for each time-bin

Methods (by class)

• boot_splines_data:

Details

Estimates a confidence interval over the difference between means (within- or between-subjects) from boot_splines_data. Confidence intervals are derived from the alpha argument in boot_splines_data (e.g., alpha = .05, CI=(.025,.975); alpha=.01, CI=(.005,.0995))

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")
                                         
# bootstrap resample 500 smoothed splines from the dataset,
# comparing females versus females at an alpha of .05                                         
df_bootstrapped <- make_boot_splines_data(response_time,
                                          predictor_column = 'Sex',
                                          within_subj = FALSE,
                                          bs_samples = 500,
                                          alpha = .05,
                                          smoother = "smooth.spline")

# analyze the divergences that occurred
boot_splines_analysis <- analyze_boot_splines(df_bootstrapped)
summary(boot_splines_analysis)

# }