Tools for Preprocessing Visual World Data
Aligns samples to a specific message.
Bins the sample data and calculates proportion looks by interest area
Output all messages with timestamps
Check which eyes were recorded during the experiment
Check the interest area IDs and labels
Check the time value(s) at a specific message
Check the number of samples in each bin
Determine the sampling rate present in the data
Check the new time series
Creates a success/failure column for each IA based on counts.
Create a time series column
Map gaze data to newly defined interest areas
Internal helper function, not intended to be called externally.
Determine downsampling options based on current sampling rate
Fast-track basic preprocessing
Create function for back-transforming empirical logits to proportions
Mark trackloss by blink and/or screen size
Plots average difference between two conditions.
Plots average contour surface of looks to a given interest area.
Plots average difference between looks to two interest areas.
Plots average looks to interest areas.
Plots diagnostic average plots of subjects/items.
Plots diagnostic plots of the empirical logit transformation.
Plots diagnostic plots of subject/item variance.
Check the classes of specific columns and re-assigns as necessary.
Recode interest area IDs and/or interest area labels
Relabel samples containing 'NA' as outside any interest area
Rename default column names for interest areas.
Checks for and removes unnecessary DV output columns.
Removes events with excessive trackloss
Select the eye used during recording
Transforms proportion looks to empirical logits.
VWPre: Tools for Preprocessing Visual World Data.
Gaze data from the Visual World Paradigm requires significant preprocessing prior to plotting and analyzing the data. This package provides functions for preparing visual world eye-tracking data for statistical analysis and plotting. It can prepare data for linear analyses (e.g., ANOVA, Gaussian-family LMER, Gaussian-family GAMM) as well as logistic analyses (e.g., binomial-family LMER and binomial-family GAMM). Additionally, it contains various plotting functions for creating grand average and conditional average plots. See the vignette for samples of the functionality. Currently, the functions in this package are designed for handling data collected with SR Research Eyelink eye trackers using Sample Reports created in SR Research Data Viewer. While we would like to add functionality for data collected with other systems in the future, the current package is considered to be feature-complete; further updates will mainly entail maintenance and the addition of minor functionality.