rwnormmix: The univariate Wrapped Normal mixtures

In BAMBI: Bivariate Angular Mixture Models

The univariate Wrapped Normal mixtures

Description

The univariate Wrapped Normal mixtures

Usage

rwnormmix(n = 1, kappa, mu, pmix)

dwnormmix(x, kappa, mu, pmix, int.displ = 3, log = FALSE)

Arguments

n	number of observations. Ignored if at least one of the other parameters have length k > 1, in which case, all the parameters are recycled to length k to produce k random variates.
kappa	vector of component concentration (inverse-variance) parameters, kappa > 0.
mu	vector of component means.
pmix	vector of mixing proportions.
x	vector of angles (in radians) where the densities are to be evaluated.
int.displ	integer displacement. If int.displ = M, then the infinite sum in the density is approximated by a sum over 2*M + 1 elements. (See Details.) The allowed values are 1, 2, 3, 4 and 5. Default is 3.
log	logical. Should the log density be returned instead?

Details

pmix, mu and kappa must be of the same length, with j-th element corresponding to the j-th component of the mixture distribution.

The univariate wrapped normal mixture distribution with component size K = length(pmix) has density

g(x) = p[1] * f(x; \kappa[1], \mu[1]) + ... + p[K] * f(x; \kappa[K], \mu[K])

where p[j], \kappa[j], \mu[j] respectively denote the mixing proportion, concentration parameter and the mean parameter for the j-th component and f(. ; \kappa, \mu) denotes the density function of the (univariate) wrapped normal distribution with mean parameter \mu and concentration parameter \kappa.

Value

dwnormmix computes the density and rwnormmix generates random deviates from the mixture density.

Examples

kappa <- 1:3
mu <- 0:2
pmix <- c(0.3, 0.3, 0.4)
x <- 1:10
n <- 10

# mixture densities calculated at each point in x
dwnormmix(x, kappa, mu, pmix)

# number of observations generated from the mixture distribution is n
rwnormmix(n, kappa, mu, pmix)

BAMBI documentation built on Oct. 25, 2024, 5:07 p.m.