acg {Riemann} | R Documentation |
Angular Central Gaussian Distribution
Description
For a hypersphere \mathcal{S}^{p-1}
in \mathbf{R}^p
, Angular
Central Gaussian (ACG) distribution ACG_p (A)
is defined via a density
f(x\vert A) = |A|^{-1/2} (x^\top A^{-1} x)^{-p/2}
with respect to the uniform measure on \mathcal{S}^{p-1}
and A
is
a symmetric positive-definite matrix. Since f(x\vert A) = f(-x\vert A)
,
it can also be used as an axial distribution on real projective space, which is
unit sphere modulo \lbrace{+1,-1\rbrace}
. One constraint we follow is that
f(x\vert A) = f(x\vert cA)
for c > 0
in that we use a normalized
version for numerical stability by restricting tr(A)=p
.
Usage
dacg(datalist, A)
racg(n, A)
mle.acg(datalist, ...)
Arguments
datalist |
a list of length- |
A |
a |
n |
the number of samples to be generated. |
... |
extra parameters for computations, including
|
Value
dacg
gives a vector of evaluated densities given samples. racg
generates
unit-norm vectors in \mathbf{R}^p
wrapped in a list. mle.acg
estimates
the SPD matrix A
.
References
Tyler DE (1987). “Statistical analysis for the angular central Gaussian distribution on the sphere.” Biometrika, 74(3), 579–589. ISSN 0006-3444, 1464-3510.
Mardia KV, Jupp PE (eds.) (1999). Directional Statistics, Wiley Series in Probability and Statistics. John Wiley \& Sons, Inc., Hoboken, NJ, USA. ISBN 978-0-470-31697-9 978-0-471-95333-3.
Examples
# -------------------------------------------------------------------
# Example with Angular Central Gaussian Distribution
#
# Given a fixed A, generate samples and estimate A via ML.
# -------------------------------------------------------------------
## GENERATE AND MLE in R^5
# Generate data
Atrue = diag(5) # true SPD matrix
sam1 = racg(50, Atrue) # random samples
sam2 = racg(100, Atrue)
# MLE
Amle1 = mle.acg(sam1)
Amle2 = mle.acg(sam2)
# Visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3), pty="s")
image(Atrue[,5:1], axes=FALSE, main="true SPD")
image(Amle1[,5:1], axes=FALSE, main="MLE with n=50")
image(Amle2[,5:1], axes=FALSE, main="MLE with n=100")
par(opar)