rvmsinmix: The bivariate von Mises sine model mixtures

In c7rishi/BAMBI: Bivariate Angular Mixture Models

The bivariate von Mises sine model mixtures

Description

The bivariate von Mises sine model mixtures

Usage

rvmsinmix(n, kappa1, kappa2, kappa3, mu1, mu2, pmix, method = "naive")

dvmsinmix(x, kappa1, kappa2, kappa3, mu1, mu2, pmix, log = FALSE)

Arguments

n	number of observations.
kappa1, kappa2, kappa3	vectors of concentration parameters; kappa1, kappa2 > 0 for each component.
mu1, mu2	vectors of mean parameters.
pmix	vector of mixture proportions.
method	Rejection sampling method to be used. Available choices are "naive" (default) or "vmprop". See details.
x	matrix of angles (in radians) where the density is to be evaluated, with each row being a single bivariate vector of angles.
log	logical. Should the log density be returned instead?

Details

All the argument vectors pmix, kappa1, kappa2, kappa3, mu1 and mu2 must be of the same length ( = component size of the mixture model), with j-th element corresponding to the j-th component of the mixture distribution.

The bivariate von Mises sine model mixture distribution with component size K = length(p.mix) has density

g(x) = ∑ p[j] * f(x; κ_1[j], κ_2[j], κ_3[j], μ_1[j], μ_2[j])

where the sum extends over j; p[j]; κ_1[j], κ_2[j], κ_3[j]; and μ_1[j], μ_2[j] respectively denote the mixing proportion, the three concentration parameters and the two mean parameter for the j-th component, j = 1, ..., K, and f(. ; κ_1, κ_2, κ_3, μ_1, μ_2) denotes the density function of the von Mises sine model with concentration parameters κ_1, κ_2, κ_3 and mean parameters μ_1, μ_2.

Value

dvmsinmix computes the density (vector if x is a two column matrix with more than one row) and rvmsinmix generates random deviates from the mixture density.

Examples

kappa1 <- c(1, 2, 3)
kappa2 <- c(1, 6, 5)
kappa3 <- c(0, 1, 2)
mu1 <- c(1, 2, 5)
mu2 <- c(0, 1, 3)
pmix <- c(0.3, 0.4, 0.3)
x <- diag(2, 2)
n <- 10

# mixture densities calculated at the rows of x
dvmsinmix(x, kappa1, kappa2, kappa3, mu1, mu2, pmix)

# number of observations generated from the mixture distribution is n
rvmsinmix(n, kappa1, kappa2, kappa3, mu1, mu2, pmix)

c7rishi/BAMBI documentation built on March 18, 2023, 6:17 p.m.