A tool for comparing the two different algorithms present in this package. This function is useful for assessing the data as well as exploring which algorithm is likely to fit data more appropriately. The raw data is run through both algorithms (using the same specified dispersion tolerances, etc.) before making comparisons of the underlying data. Can only be used for single participant data.
compare_algorithms(
data,
plot_fixations = TRUE,
print_summary = TRUE,
sample_rate = NULL,
threshold = 100,
min_dur = 150,
min_dur_sac = 20,
disp_tol = 100,
NA_tol = 0.25,
smooth = FALSE
)a list of the fixation data, correlation output, and data used for plotting
A dataframe with raw data (time, x, y, trial) for one participant
Whether to plot the detected fixations. default as TRUE
Whether to print the summary table. default as TRUE
sample rate of the eye-tracker. If default of NULL, then it will be computed from the timestamp data and the number of samples. Supplied to the VTI algorithm
velocity threshold (degrees of VA / sec) to be used for identifying saccades. Supplied to the VTI algorithm
Minimum duration (in milliseconds) of period over which fixations are assessed. Supplied to both algorithms.
Minimum duration (in milliseconds) for saccades to be determined. Supplied to the VTI algorithm
Maximum tolerance (in pixels) for the dispersion of values allowed over fixation period. Supplied to both algorithms
the proportion of NAs tolerated within any window of samples that is evaluated as a fixation. Supplied to the dispersion algorithm
include a call to eyetools::smoother on each trial. Supplied to the VTI algorithm
# \donttest{
data <- combine_eyes(HCL)
data <- interpolate(data)
compare_algorithms(data[data$pID == 119,])
# }
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