make_boot_splines_data {eyetrackingR}
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 |
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)
-
time_sequence_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") df_bootstrapped <- make_boot_splines_data(response_time, predictor_column = 'Sex', within_subj = FALSE, bs_samples = 500, alpha = .05, smoother = "smooth.spline")