analyze_boot_splines {eyetrackingR}
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 class 'boot_splines_data' analyze_boot_splines(data)
Arguments
data |
The output of the |
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))
Value
A dataframe indicating means and CIs for each time-bin
Methods (by class)
-
boot_splines_data
:
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) 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)
[Package eyetrackingR version 0.1.3]