mlbetapr: Beta prime distribution maximum likelihood estimation

In JonasMoss/univariateML: Maximum Likelihood Estimation for Univariate Densities

Beta prime distribution maximum likelihood estimation

Description

This function does not estimate the scale parameter for the BetaPrime distribution. Transforms the data and uses stat::nlm to estimate the parameters of the Beta distribution.

Usage

mlbetapr(x, na.rm = FALSE, ...)

Arguments

x	a (non-empty) numeric vector of data values.
na.rm	logical. Should missing values be removed?
...	passed to mlbeta.

Details

For the density function of the Beta prime distribution see BetaPrime.

For type, the option none is fastest.

Value

mlbetapr returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for shape1 and shape2 and the following attributes:

model	The name of the model.
density	The density associated with the estimates.
logLik	The loglikelihood at the maximum.
support	The support of the density.
n	The number of observations.
call	The call as captured my match.call

References

Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, Volume 2, Chapter 25. Wiley, New York.

See Also

BetaPrime for the Beta prime density, nlm for the optimizer this function uses, mlbeta for the Beta distribution maximum likelihood estimator.

Examples

AIC(mlbetapr(USArrests$Rape))

JonasMoss/univariateML documentation built on Nov. 3, 2024, 3:03 p.m.