The mousetrap package provides functions for importing, preprocessing, analyzing, aggregating, and visualizing mouse-tracking data. In the following, a brief overview of the functions in this package is given.
The first step is to create a mousetrap data object. Depending on the input format, one of the following functions can be used. A detailed description (and example) of the resulting mousetrap data object can be found in mt_example.
mt_import_mousetrap imports mouse-tracking data that were recorded using the mousetrap plug-ins for OpenSesame.
mt_import_wide imports mouse-tracking data saved in a wide format (e.g., data exported from MouseTracker).
mt_import_long imports mouse-tracking data saved in a long format. (e.g., trajectories exported using the mt_reshape function from this package).
A number of functions are available that perform preprocessing operations typically used before analyzing mouse-tracking data.
mt_remap_symmetric remaps mouse trajectories to one side (or one quadrant) of the coordinate system.
mt_exclude_initiation excludes the initial phase of a trial without mouse movement.
mt_align_start aligns the start position of trajectories.
mt_space_normalize performs space-normalization by remapping all trajectories so that they share a common initial and final coordinate.
mt_time_normalize performs time-normalization, resulting in an identical number of samples for all trajectories.
mt_resample resamples trajectories such that samples occur at constant intervals of a specified length.
mt_average averages trajectory coordinates for time bins of constant duration.
mt_subset filters mouse-tracking data by trials, such that are only those meeting defined criteria are included.
mt_add_variables adds new, self created variables to a trajectory array.
A number of different analysis procedures and summary statistics for mouse trajectories have been established in the existing literature. The following functions implement many of these approaches.
mt_derivatives calculates distance, velocity, and acceleration for trajectories.
mt_deviations calculates the deviations from an idealized trajectory (straight line).
mt_measures calculates a set of mouse-tracking measures.
mt_sample_entropy calculates sample entropy.
mt_movement_angle calculates the initial movement angle.
mt_standardize standardizes mouse-tracking measures onto a common scale (individually for subsets of the data, e.g. z-scaled data per participant).
mt_check_bimodality assesses the bimodality of mouse-tracking measure distributions.
mt_check_resolution checks the (temporal) logging resolution of raw trajectories.
A number of helper functions are provided for aggregating, plotting, and exporting the multi-dimensional mouse trajectory arrays.
mt_reshape is a general purpose reshaping and aggregation function for mousetrap data.
mt_aggregate aggregates mouse-tracking data per condition.
mt_aggregate_per_subject aggregates mouse-tracking data per (within subjects-) condition separately for each subject.
mt_export_long exports mouse-tracking data in long format.
mt_export_wide exports mouse-tracking data in wide format.
The following functions can be used for plotting trajectory data, e.g., individual and aggregated trajectories or velocity profiles.
mt_plot plots individual trajectory data.
mt_plot_aggregate plots aggregated trajectory data.
mt_plot_per_trajectory creates a pdf with separate plots per trajectory.
mt_plot_add_rect adds rectangles to a trajectory plot.
mt_plot_riverbed plots the relative frequency of a selected variable across time.
bimodality_coefficient calculates the bimodality coefficient.
scale_within scales and centers variables within the levels of another variable.
read_mousetracker reads data that was exported from MouseTracker.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
mt_example <- mt_import_mousetrap(mt_example_raw) mt_example <- mt_remap_symmetric(mt_example) mt_example <- mt_align_start(mt_example) mt_example <- mt_time_normalize(mt_example) mt_example <- mt_derivatives(mt_example) mt_example <- mt_deviations(mt_example) mt_example <- mt_measures(mt_example) average_measures <- mt_aggregate( mt_example, use="measures", use_variables=c("MAD", "AD"), use2_variables="Condition" ) mt_plot(mt_example, use="tn_trajectories", x="xpos", y="ypos", color="Condition" ) mt_plot_aggregate(mt_example, use="tn_trajectories", x="xpos", y="ypos", color="Condition" )