Simulation of random values from a spherical Fisher-Bingham distribution
Simulation of random values from a spherical Fisher-Bingham distribution.
rfb(n, k, m, A)
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). |
Random values from a spherical Fisher-Bingham distribution are generated. This functions included the option of simulating from a Kent distribution also.
A matrix with the simulated data.
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Giorgos Athineou <gioathineou@gmail.com>
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
k <- 15 mu <- rnorm(3) mu <- mu / sqrt( sum(mu^2) ) A <- cov(iris[, 1:3]) x <- rfb(50, k, mu, A) vmf.mle(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.mle(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.mle(x) ## fits a von Mises-Fisher distribution
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