Mouse Trajectory Analyses for Behavioural Scientists.
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 awesomesignal
package. Thus, you do not have to calllibrary("signal")
. Such time-saving, much wow. Also, not having to attach thesignal
package avoids ambiguity betweensignal::filter()
anddplyr::filter()
in your search path.interp2()
is a convenience wrapper tointerp1()
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.