The von Mises Distribution

Density, probability distribution function, quantiles, and random generation for the circular normal distribution with mean and kappa.

Usage

rvm(n, mean, kappa)

dvm(theta, mean, kappa, log = FALSE, axial = FALSE)

pvm(theta, mean, kappa, from = NULL, tol = 1e-20)

qvm(p, mean = 0, kappa, from = NULL, tol = .Machine$double.eps^(0.6), ...)

Arguments

n

integer. Number of observations in degrees

mean

numeric. Mean angle in degrees

kappa

numeric. Concentration parameter in the range (0, Inf]

theta

numeric. Angular value in degrees

log

logical. If TRUE, probabilities p are given as log(p).

axial

logical. Whether the data are axial, i.e. \(\pi\)-periodical (TRUE, the default) or directional, i.e. \(2 \pi\)-periodical (FALSE).

from

if NULL is set to \(\mu-\pi\). This is the value from which the pvm and qvm are evaluated. in degrees.

tol

numeric. The precision in evaluating the distribution function or the quantile.

p

numeric. Vector of probabilities with values in \([0,1]\).

...

parameters passed to stats::integrate().

Value

dvm gives the density, pvm gives the probability of the von Mises distribution function, rvm generates random deviates (in degrees), and qvm provides quantiles (in degrees).

Examples

set.seed(1)
x <- rvm(5, mean = 90, kappa = 2)

dvm(x, mean = 90, kappa = 2)
dvm(x, mean = 90, kappa = 2, axial = TRUE)
#> [1] 0.71367193 0.14001117 0.06570123 0.88231652 0.01932479

pvm(x, mean = 90, kappa = 2)
#> [1] 0.6542335 0.9908071 0.1148932 0.3986252 0.9568411
qvm(c(.25, .5, .75), mean = 90, kappa = 2)
#> [1]  59.65254  90.00000 120.34746