vmf.mle: MLE of (hyper-)spherical rotationally symmetric distributions

In Directional: A Collection of Functions for Directional Data Analysis

MLE of (hyper-)spherical rotationally symmetric distributions

Description

MLE of (hyper-)spherical rotationally symmetric distributions.

Usage

vmf.mle(x, fast = FALSE, tol = 1e-07)
multivmf.mle(x, ina, tol = 1e-07, ell = FALSE)
iag.mle(x, tol = 1e-06)
sipc.mle(x, tol = 1e-6)
acg.mle(x, tol = 1e-07)
spcauchy.mle(x, tol = 1e-06)
spcauchy.mle2(x, tol = 1e-06)
pkbd.mle(x, tol = 1e-6)
pkbd.mle2(x, tol = 1e-6)

Arguments

x	A matrix with directional data, i.e. unit vectors.
fast	IF you want a faster version, but with fewer information returned, set this equal to TRUE.
ina	A numerical vector with discrete numbers starting from 1, i.e. 1, 2, 3, 4,... or a factor variable. Each number denotes a sample or group. If you supply a continuous valued vector the function will obviously provide wrong results.
ell	This is for the multivmf.mle only. Do you want the log-likelihood returned? The default value is TRUE.
tol	The tolerance value at which to terminate the iterations.

Details

The vmf.mle() estimates the mean direction and concentration of a fitted von Mises-Fisher distribution.

The von Mises-Fisher distribution for groups of data is also implemented.

The acg.mle() fits the angular central Gaussian distribution. There is a constraint on the estimated covariance matrix; its trace is equal to the number of variables. An iterative algorithm takes place and convergence is guaranteed.

The iag.mle() implements MLE of the spherical projected normal distribution, for spherical and hyper-spherical data.

The spcauchy.mle() is faster than the spcacuhy.mle2() because it employs the Newton-Raphson algortihm. Both functions estimate the parameters of the spherical Cauchy distribution, for any dimension. Despite the name sounds confusing, it is implemented for arbitrary dimensions, not only the sphere. The function employs a combination of the fixed points iteration algorithm and the Brent algorithm.

The pkbd.mle() estimates the parameters of the Poisson kernel-based distribution (PKBD), for any dimension and it is faster than pkbd.mle2() for the same reason with the spcauchy.mle().

The sipc.mle() implements MLE of the spherical independent projected Cauchy distribution, for spherical data only.

Value

For the von Mises-Fisher a list including:

loglik	The maximum log-likelihood value.
mu	The mean direction.
kappa	The concentration parameter.

For the multi von Mises-Fisher a list including:

loglik	A vector with the maximum log-likelihood values if ell is set to TRUE. Otherwise NULL is returned.
mi	A matrix with the group mean directions.
ki	A vector with the group concentration parameters.

For the angular central Gaussian a list including:

iter	The number if iterations required by the algorithm to converge to the solution.
cova	The estimated covariance matrix.

For the spherical projected normal a list including:

iters	The number of iteration required by the Newton-Raphson.
mesi	A matrix with two rows. The first row is the mean direction and the second is the mean vector. The first comes from the second by normalising to have unit length.
param	A vector with the elements, the norm of mean vector, the log-likelihood and the log-likelihood of the spherical uniform distribution. The third value helps in case you want to do a log-likelihood ratio test for uniformity.

For the spherical Cauchy and the PKBD a list including:

mesos	The mean in R^{d+1}. See Tsagris and Alenazy (2023) for a re-parametrization that applies in the spherical Cauchy also.
mu	The mean direction.
gamma	The norm of the mean in R^{d+1}. See Tsagris and Alenazy (2023) for a re-parametrization that applies in the spherical Cauchy also.
rho	The concetration parameter, this takes values in [0, 1).
loglik	The log-likelihood value.

For the SIPC a list including:

mu	The mean direction.
loglik	The log-likelihood value.

For the angular central Gaussian a list including:

iter	The number of iterations performed.
cova	The covariance matrix.

Author(s)

Michail Tsagris and Zehao Yu.

R implementation and documentation: Michail Tsagris [e-mail] and Zehao Yu [e-mail].

References

Mardia K. V. and Jupp P. E. (2000). Directional statistics. Chicester: John Wiley & Sons.

Sra S. (2012). A short note on parameter approximation for von Mises-Fisher distributions: and a fast implementation of I_s(x). Computational Statistics, 27(1): 177–190.

Tyler D. E. (1987). Statistical analysis for the angular central Gaussian distribution on the sphere. Biometrika 74(3): 579–589.

Paine P.J., Preston S.P., Tsagris M. and Wood A.T.A. (2018). An Elliptically Symmetric Angular Gaussian Distribution. Statistics and Computing, 28: 689–697.

Tsagris M. and Alzeley O. (2024). Circular and spherical projected Cauchy distributions: A Novel Framework for Circular and Directional Data Modeling. https://arxiv.org/pdf/2302.02468.pdf

Kato S. and McCullagh P. (2020). Some properties of a Cauchy family on the sphere derived from the Mobius transformations. Bernoulli, 26(4): 3224–3248. https://arxiv.org/pdf/1510.07679.pdf

Golzy M. and Markatou M. (2020). Poisson kernel-based clustering on the sphere: convergence properties, identifiability, and a method of sampling. Journal of Computational and Graphical Statistics, 29(4): 758–770.

Sablica L., Hornik K. and Leydold J. (2023). Efficient sampling from the PKBD distribution. Electronic Journal of Statistics, 17(2): 2180–2209.

Zehao Yu and Xianzheng Huang (2024). A new parameterization for elliptically symmetric angular Gaussian distributions of arbitrary dimension. Electronic Journal of Statististics, 18(1): 301–334.

Tsagris M. and Papastamoulis P. (2024). Directional data analysis using the spherical Cauchy and the Poisson kernel-based distribution. https://arxiv.org/pdf/2409.03292.

See Also

racg, rvmf

Examples

m <- c(0, 0, 0, 0)
s <- cov(iris[, 1:4])
x <- racg(100, s)
mod <- acg.mle(x)
mod
cov2cor(mod$cova)  ## estimated covariance matrix turned into a correlation matrix
cov2cor(s)  ## true covariance matrix turned into a correlation matrix
vmf.mle(x)
x <- rbind( rvmf(100,rnorm(4), 10), rvmf(100,rnorm(4), 20) )
a <- multivmf.mle(x, rep(1:2, each = 100) )

Directional documentation built on Oct. 30, 2024, 9:15 a.m.