sirt (version 4.1-15)

dirichlet.mle: Maximum Likelihood Estimation of the Dirichlet Distribution

Description

Maximum likelihood estimation of the parameters of the Dirichlet distribution

Usage

dirichlet.mle(x, weights=NULL, eps=10^(-5), convcrit=1e-05, maxit=1000,
     oldfac=.3, progress=FALSE)

Value

A list with following entries

alpha

Vector of \(\alpha\) parameters

alpha0

The concentration parameter \(\alpha_0=\sum_k \alpha_k\)

xsi

Vector of proportions \(\xi_k=\alpha_k / \alpha_0\)

Arguments

x

Data frame with \(N\) observations and \(K\) variables of a Dirichlet distribution

weights

Optional vector of frequency weights

eps

Tolerance number which is added to prevent from logarithms of zero

convcrit

Convergence criterion

maxit

Maximum number of iterations

oldfac

Convergence acceleration factor. It must be a parameter between 0 and 1.

progress

Display iteration progress?

References

Minka, T. P. (2012). Estimating a Dirichlet distribution. Technical Report.

See Also

For simulating Dirichlet vectors with matrix-wise \(\bold{\alpha}\) parameters see dirichlet.simul.

For a variety of functions concerning the Dirichlet distribution see the DirichletReg package.