Mouse Trajectory Analyses for Behavioural Scientists

Helping psychologists and other behavioural scientists to analyze mouse movement (and other 2-D trajectory) data. Bundles together several functions that compute spatial measures (e.g., maximum absolute deviation, area under the curve, sample entropy) or provide a shorthand for procedures that are frequently used (e.g., time normalization, linear interpolation, extracting initiation and movement times). For more information on these dependent measures, see Wirth et al. (2020) .

mousetRajectory: Trajectory Analyses for Behavioural Scientists

Tool helping psychologists and other behavioural scientists to analyze mouse movement (and other 2-D trajectory) data. Bundles together several functions computing spatial measures (maximum absolute deviation, area under the curve, sample entropy) or providing a shorthand for often-used procedures.

Installation

You can install mousetRajectory from CRAN with

install.packages("mousetRajectory")

Alternatively, you can keep up to date and install the latest development version of mousetRajectory from github.com/mc-schaaf/mousetRajectory with:

if(!require("devtools")){install.packages("devtools")}
devtools::install_github("mc-schaaf/mousetRajectory")

Function Overview

Currently, the following functions are featured:

• Preprocessing:
  • is_monotonic() checks whether your timestamps make sense and warns you if they don’t.
  • is_monotonic_along_ideal() checks whether your trajectories make sense and warns you if they don’t.
  • time_circle_left() tells you the time at which the starting area was left.
  • time_circle_entered() tells you the time at which the end area was entered.
  • point_crosses() tells you how often a certain value on the x or y axis is crossed.
  • direction_changes() tells you how often the direction along the x or y axis changes.
  • interp1() directs you to the interpolation function from the awesome signal package. Thus, you do not have to call library("signal"). Such time-saving, much wow. Also, not having to attach the signal package avoids ambiguity between signal::filter() and dplyr::filter() in your search path.
  • interp2() is a convenience wrapper to interp1() that rescales the time for you.
• Spatial measures:
  • starting_angle() computes (not only starting) angles.
  • auc() computes the (signed) Area Under the Curve (AUC).
  • max_ad() computes the (signed) Maximum Absolute Deviation (MAD).
  • curvature() computes the curvature.
  • index_max_velocity() computes the time to peak velocity, assuming equidistant times between data points.
  • index_max_acceleration() computes the time to peak acceleration, assuming equidistant times between data points.
• Other measures
  • sampen() computes the sample entropy.

Documentation

You can find an example application as well as the full documentation at mc-schaaf.github.io/mousetRajectory/.

Bug Reports

Please report bugs to github.com/mc-schaaf/mousetRajectory/issues.