Column-wise MLE of the angular Gaussian distribution
Column-wise MLE of the angular Gaussian distribution.
colspml.mle(x ,tol = 1e-07, maxiters = 100, parallel = FALSE)
x |
A numerical matrix with data. Each column refers to a different vector of observations of the same distribution. The values of for Lognormal must be greater than zero, for the logitnormal they must by percentages, exluding 0 and 1, whereas for the Borel distribution the x must contain integer values greater than 1. |
tol |
The tolerance value to terminate the Newton-Raphson algorithm. |
maxiters |
The maximum number of iterations that can take place in each regression. |
parallel |
Do you want this to be executed in parallel or not. The parallel takes place in C++, and the number of threads is defined by each system's availiable cores. |
For each column, spml.mle function is applied that fits the angular Gaussian distribution estimates its parameters and computes the maximum log-likelihood.
A matrix with four columns. The first two are the mean vector, then the γ parameter, and the fourth column contains maximum log-likelihood.
Stefanos Fafalios
R implementation and documentation: Stefanos Fafalios stefanosfafalios@gmail.com
Presnell Brett, Morrison Scott P. and Littell Ramon C. (1998). Projected multivariate linear models for directional data. Journal of the American Statistical Association, 93(443): 1068-1077.
x <- matrix( runif(100 * 10), ncol = 10) a <- colspml.mle(x) x <- NULL
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