mlbeta: Beta distribution maximum likelihood estimation

In JonasMoss/univariateML: Maximum Likelihood Estimation for Univariate Densities

Beta distribution maximum likelihood estimation

Description

Uses stat::nlm to estimate the parameters of the Beta distribution.

Usage

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

Arguments

x	a (non-empty) numeric vector of data values.
na.rm	logical. Should missing values be removed?
...	start contains optional starting parameter values for the minimization, passed to the stats::nlm function. type specifies whether a dedicated "gradient", "hessian", or "none" should be passed to stats::nlm.

Details

For the density function of the Beta distribution see Beta.

For type, the option none is fastest.

Value

mlbeta 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

Beta for the Beta density, nlm for the optimizer this function uses.

Examples

AIC(mlbeta(USArrests$Rape / 100))

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