analyze_boot_splines {eyetrackingR} | R Documentation |
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)
-
analyze_boot_splines(boot_splines_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")
# 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)
## End(Not run)
[Package eyetrackingR version 0.2.1 Index]