-
Notifications
You must be signed in to change notification settings - Fork 1
/
README.Rmd
74 lines (54 loc) · 3.5 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
---
output: github_document
---
<!-- README.md is generated from README.Rmd. EDIT THAT FILE!!! -->
<!-- badges: start -->
[](https://github.com/mc-schaaf/mousetRajectory/actions/workflows/R-CMD-check.yaml)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://CRAN.R-project.org/package=mousetRajectory)
[](https://zenodo.org/badge/latestdoi/567672427)
<!-- badges: end -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# 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
``` r
install.packages("mousetRajectory")
```
Alternatively, you can keep up to date and install the latest development version of mousetRajectory from [github.com/mc-schaaf/mousetRajectory](https://github.com/mc-schaaf/mousetRajectory) with:
``` r
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/](https://mc-schaaf.github.io/mousetRajectory/articles/mousetRajectory.html).
## Bug Reports
Please report bugs to [github.com/mc-schaaf/mousetRajectory/issues](https://github.com/mc-schaaf/mousetRajectory/issues).