Nakagami distribution maximum likelihood estimation

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.

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?

Details

For the density function of the Nakagami distribution see Nakagami.

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)
#> Maximum likelihood estimates for the Gamma model 
#>  shape    rate  
#> 4.7171  0.1352