fitdirichlet: Fitting a Dirichlet distribution

In compositions: Compositional Data Analysis

Fitting a Dirichlet distribution

Description

Fits a Dirichtlet Distribution to a dataset by maximum likelihood.

Usage

fitDirichlet(x,elog=mean(ult(x)),alpha0=rep(1,length(elog)),maxIter=20,n=nrow(x))

Arguments

x	a dataset of compositions (acomp)
elog	the expected log can provided instead of the dataset itself.
alpha0	the start value for alpha parameter in the iteration
maxIter	The maximum number of iterations in the Fischer scoring method.
n	the number of datapoints used to estimate elog

Details

The fitting is done using a modified version of the Fisher-Scoring method using analytiscal expressions for log mean and log variance. The modification is introducted to prevent the algorithm from leaving the admissible parameter set. It reduced the stepsize to at most have of distance to the limit of the admissible parameter set.

Value

alpha	the estimated parameter
loglikelihood	the likelihood
df	The dimension of the dataset minus the dimension of the parameter

Missing Policy

Up to now the fitting can not handle missings.

Author(s)

K.Gerald v.d. Boogaart http://www.stat.boogaart.de

References

Aitchison, J. (1986) The Statistical Analysis of Compositional Data Monographs on Statistics and Applied Probability. Chapman & Hall Ltd., London (UK). 416p.

See Also

rDirichlet, acompDirichletGOF.test, runif.acomp, rnorm.acomp,

Examples

x <- rDirichlet.acomp(100,c(1,2,3,4))
fitDirichlet(x)

compositions documentation built on June 22, 2024, 12:15 p.m.