rwnormmix {BAMBI} | R Documentation |
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, |
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 |
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)