Description
Circular Analyses Helper Functions.
Description
Light-weight functions for computing descriptive statistics in different circular spaces (e.g., 2pi, 180, or 360 degrees), to handle angle-dependent biases, pad circular data, and more. Specifically aimed for psychologists and neuroscientists analyzing circular data. Basic methods are based on Jammalamadaka and SenGupta (2001) <doi:10.1142/4031>, removal of cardinal biases is based on the approach introduced in van Bergen, Ma, Pratte, & Jehee (2015) <doi:10.1038/nn.4150> and Chetverikov and Jehee (2023) <doi:10.1038/s41467-023-43251-w>.
README.md
circhelp
circhelp is a small helper package for circular data analyses in R, particularly useful for cognitive studies on orientation, motion direction, and other circular features. The package contains functions for descriptive statistics for circular data (computing means, SD, and skewness), angular differences, and correlation. It also includes a function to correct for cardinal biases in the human estimates of circular features (e.g., orientation).
Installation
You can install the current version from GitHub with:
# install.packages("devtools")
devtools::install_github("achetverikov/circhelp")
Usage
Most functions are self-explanatory.
library(circhelp)
#> Loading required package: data.table
#> Loading required package: ggplot2
library(mgcv)
#> Loading required package: nlme
#> This is mgcv 1.9-1. For overview type 'help("mgcv-package")'.
# compute a set of descriptive statistics
x <- rnorm(500)
circ_descr(x)
#> $mu
#> [1] 0.07084994
#>
#> $sigma
#> [1] 0.9691056
#>
#> $skew_pewsey
#> [1] 0.0118935
#>
#> $skew_fischer
#> [1] -0.04671673
#>
#> $rho
#> [1] 0.6252631
#>
#> $skew_rel_to_zero
#> [1] 0.03426532
# compute difference in orientations
a <- 5
b <- 170
angle_diff_180(a, b)
#> [1] 15
# compute difference in 360° space (e.g., motion directions)
angle_diff_360(a, b)
#> [1] -165
# compute correlation between angles
data <- rmvn(10000, c(0, 0), V = matrix(c(1, 0.5, 0.5, 1), ncol = 2))
circ_corr(data[, 1], data[, 2])
#> [1] 0.4392524
The only (somewhat) complicated function is remove_cardinal_biases, see the help files and the vignette for an example use case. 