
Angular central Gaussian random values simulation.
racg(n, sigma)
The sample size, a numerical value.
The covariance matrix in
A matrix with the simulated data.
The algorithm uses univariate normal random values and transforms them to multivariate via a spectral decomposition. The vectors are then scaled to have unit length.
Tyler D. E. (1987). Statistical analysis for the angular central Gaussian distribution on the sphere. Biometrika 74(3): 579-589.
# NOT RUN {
s <- cov( iris[, 1:4] )
x <- racg(100, s)
acg(x)
vmf(x) ## the concentration parameter, kappa, is very low, close to zero, as expected.
# }
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