Event Classification, Visualization and Analysis of Eye Tracking Data

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Documentation for package ‘kollaR’ version 1.1.1

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adjust_fixation_timing Adjust the onset and offset of fixations to avoid misclassification of saccade samples as belonging to fixations
algorithm_adaptive Adaptive velocity-based algorithm for saccade and fixation detection
algorithm_i2mc Fixation detection by two-means clustering
algorithm_idt Dispersion-based fixation detection algorithm '(I-DT)'
algorithm_ivt I-VT algorithm for fixation and saccade detection
animated_fixation_plot Create GIF animation of fixations on a stimulus images
aoi_test Test whether a gaze coordinates are within or outside a rectangular or elliptical AOI. The aois df must contain the variables x0, x1, y0 and y1. x0 is the minimum x value, y0 the minimum y value. x1 the maximum x value. y1 the maximum y value and type where rect means that the AOI is a rectangle and circle that the AOI is a circle or ellipse If a column called name is present, the output for each AOI will be labelled accordingly. Otherwise, the output will be labelled according to the order of the AOI in the data frame. The df 'gaze' must contain the variables onset, duration, x, and y. Latency will be defined as the value in onset of the first detected gaze coordinate in the AOI Make sure that the timestamps are correct! The function can be used with gaze data either fixations, saccades, or single samples. Note that the output variables are not equally relevant for all types of gaze data. For example, both total duration and latency are relevant in many analyses focusing on fixations, but total duration may be less relevant in analyses of saccades.
cluster2m Fixation detection by two-means clustering
downsample_gaze Downsample gaze
draw_aois Draw one or more areas of interest, AOIs, on a stimulus image and save to the R prompt. The input is the path to a 2D image. Supported file formats: JPEG, BMP, PNG. The function returns a data frame with all saved AOIs. By default, AOIs are drawn in a coordinate system where y is 0 in the lower extreme of the image, e.g., an ascending y axis. Tobii eye trackers use a coordinate system with a descending y-axis, e.g., x and y are 0 in the upper left corner of the image. Make sure that your AOIS match the coordinate system of your eye tracker output. By setting the parameter reverse.y.axis to TRUE, the saved AOIs will be reformatted to fit a coordinate system with a descending y-axis. All AOIS have the variables x0, x1, y0 and y1. x0 is the minimum x value, y0 the minimum y value. x1 the maximum x value. y1 the maximum y value
filt_plot_2d Plot fixations vs. individual sample coordinates in 2D space. In the current release, filt_plot_2d is a wrapper around fixation_plot_2d which accepts the same arguments.
filt_plot_temporal Plot fixation filtered vs. raw gaze coordinates. This function will be replaced by fixation_plot_temporal in future releases. It is currently a wrapper around fixation_plot_temporal accepting the same arguments.
find.transition.weights Find transition weights for each sample in a gaze matrix.
find.valid.periods Find subsequent periods in a vector with values below a threshold. Used internally by the function suggest_threshold
fixation_plot_2d Plot fixations vs. individual sample coordinates in 2D space.
fixation_plot_temporal Plot fixation classified vs. raw gaze coordinates
fixation_plot_ts Plot fixation classified vs. raw gaze coordinate time series
idt_filter Dispersion-based fixation detection algorithm '(I-DT)'
interpolate_with_margin Interpolate over gaps (subsequent NAs) in vector.
ivt_filter I-VT algorithm for fixation and saccade detection
kollaR Fixation and Saccade Detection, Visualization, and Analysis of Eye Tracking Data
merge_adjacent_fixations Merge adjacent fixations
plot_algorithm_results Plot vdescriptives one or more fixation detection algorithms
plot_filter_results Plot descriptives from one or more fixation detection algorithms
plot_sample_velocity Plot the sample-to-sample velocity of eye tracking data.
plot_velocity_profiles Create ggplot of saccade velocity profiles
preprocess_gaze Interpolation and smoothing of gaze-vector
process_gaze Interpolation and smoothing of gaze-vector. This function will be replaced by preprocess_gaze in future versions. process_gaze is a wrapper around preprocess gaze (the two functions produce the same result)
sample.data.classified Sample-to-sample raw and fixation classified data from 1 individual
sample.data.fixation1 Fixations from 1 individual
sample.data.fixations Fixations from 7 individuals
sample.data.processed Pre-processed sample-by-sample example data
sample.data.saccades Saccades from 3 individuals
sample.data.unprocessed Unprocessed sample-by-sample example data
static_plot Plot fixations in 2D space overlaied on a stimulus image
suggest_threshold Data-driven identification of threshold parameters for adaptive veloctity-based saccade detection.
summarize_fixation_metrics Summarize fixation statistics
trim_fixations Adjust the onset and offset of fixations to avoid misclassification of saccade samples as belonging to fixations