Filtering, Visualization and Analysis of Eye Tracking Data

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Documentation for package ‘kollaR’ version 1.0.3

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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 fixation filtered vs. raw or unfiltered gaze coordinates in 2D space.
filt_plot_temporal Plot fixation filtered vs. raw gaze coordinates
find.transition.weights Find transition weights for each sample in a gaze matrix.
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 Filtering, Visualization, and Analysis of Eye Tracking Data
merge_adjacent_fixations Merge adjacent fixations
plot_filter_results Plot validity measures 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
process_gaze Interpolation and smoothing of gaze-vector
sample.data.filtered Fixation-filtered sample-by-sample example data
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