maxmult: Estimation of the multivariate beta distribution

Description Usage Arguments Details Note Author(s) References See Also Examples

Description

Gives a maximum likelihood estimate for the parameters of a Dirichlet distribution, on the basis of datapoints drawn from a multivariate beta distribution

Usage

1
maxmult(M, start_a = NULL, give = FALSE, method = "nlm", ...)

Arguments

M

Integer matrix whose rows give multinomial observations

start_a

Start point for optimization, with default NULL being interpreted as Mosimann's formula 29

give

Boolean, with default FALSE meaning to return just the point estimate and TRUE meaning to return all the output from the optimization routine

method

Text string specifying the optimization routine to use. Two values coded: default nlm means to use nlm() and optim meaning to use optim(); anything else means to return Mosimann's estimate (equation 29)

...

Further arguments passed to nlm() or optim()

Details

Finds the maximum likelihood estimate from the equation 7 of Mosimann 1962.

Note

The nlm() function appears to be better suited to this problem than optim().

Author(s)

Robin K. S. Hankin

References

J. E. Mosimann 1962. “On the compound multinomial distribution, the multivariate beta-distribution, and correlations among proportions”. Biometrika, volume 49, numbers 1 and 2, pp65-82.

See Also

pollen

Examples

1
2
data(pollen)
maxmult(pollen, start_a=c(51.81, 0.987, 5.332, 1.961))

hyperdirichlet documentation built on May 19, 2017, 9:42 a.m.

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