mlnaka: Nakagami distribution maximum likelihood estimation

In JonasMoss/univariateML: Maximum Likelihood Estimation for Univariate Densities

Nakagami distribution maximum likelihood estimation

Description

The maximum likelihood estimates of shape and scale are calculated by calling mlgamma on the transformed data.

Usage

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

Arguments

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

Details

For the density function of the Nakagami distribution see Nakagami.

Value

mlgamma returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for shape and rate 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 by match.call
continuous	Is the density continuous or discrete?

References

Choi, S. C, and R. Wette. "Maximum likelihood estimation of the parameters of the gamma distribution and their bias." Technometrics 11.4 (1969): 683-690.

Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, Volume 1, Chapter 17. Wiley, New York.

See Also

Nakagami for the Nakagami distribution. GammaDist for the closely related Gamma density. See mlgamma for the machinery underlying this function.

Examples

mlgamma(precip)

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