R: Event Classification, Visualization and Analysis of Eye Tracking Data

# Event Classification, Visualization and Analysis of Eye Tracking Data

## Documentation for package ‘kollaR’ version 1.1.4

- DESCRIPTION file.

## Help Pages

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 data fram aois must contain variables with minimum x, minimum y, maximum x, and maximum y values and type where rect or rectangle means that the AOI is a rectangle and ellipse or circle that the AOI is an ellipse The coordinate variables must be named: x0 OR xmin: minimum x value x1 OR xmax: maximum x value y0 OR ymin: minimum y value y1 OR ymax: maximum y value 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.
calculate_rms | Calculate sample-to-sample root mean squared deviations (RMS) of subsequent samples in a data segment
cluster2m | Fixation detection by two-means clustering
downsample_gaze | Downsample gaze
draw_aois | Draw one or more areas of interest, AOIs, on one or more stimulus images and save the coordinates to the R prompt. 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 xmin, xmax, ymin and ymax. xmin is the minimum x value, ymin the minimum y value. xmax the maximum x value. ymax the maximum y value. Major updates to this function were made in version 1.1.4
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
movmean.filter | Calculate the moving mean of a vector
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 on a stimulus image (with visible x and y axis labels and ticks)
static_plot_minimal | Plot fixations in 2D space on a stimulus image (minimalistic plot)
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