Directional (version 2.7)

Simulation of random values from a spherical Fisher-Bingham distribution: Simulation of random values from a spherical Fisher-Bingham distribution

Description

Simulation of random values from a spherical Fisher-Bingham distribution.

Usage

rfb(n, k, m, A)

Arguments

n

The sample size.

k

The concentraion parameter (Fisher part). It has to be greater than 0.

m

The mean direction (Fisher part).

A

A symmetric matrix (Bingham part).

Value

A matrix with the simulated data.

Details

Random values from a spherical Fisher-Bingham distribution are generated. This functions included the option of simulating from a Kent distribution also.

References

Kent J.T., Ganeiber A.M. and Mardia K.V. (2013). A new method to simulate the Bingham and related distributions in directional data analysis with applications. http://arxiv.org/pdf/1310.8110v1.pdf

See Also

rbingham, rvmf, rkent, f.rbing

Examples

# NOT RUN {
k <- 15
mu <- rnorm(3)
mu <- mu / sqrt( sum(mu^2) )
A <- cov(iris[, 1:3])
x <- rfb(50, k, mu, A)
vmf(x) ## fits a von Mises-Fisher distribution to the simulated data
## Next we simulate from a Kent distribution
A <- diag( c(-5, 0, 5) )
n <- 100
x <- rfb(n, k, mu, A) ## data follow a Kent distribution
kent.mle(x) ## fits a Kent distribution
vmf(x) ## fits a von Mises-Fisher distribution
A <- diag( c(5, 0, -5) )
n <- 100
x <- rfb(n, k, mu, A) ## data follow a Kent distribution
kent.mle(x) ## fits a Kent distribution
vmf(x) ## fits a von Mises-Fisher distribution
# }