mllgamma: Log-gamma distribution maximum likelihood estimation

In univariateML: Maximum Likelihood Estimation for Univariate Densities

Description

The maximum likelihood estimate of shapelog and ratelog are calculated by calling mlgamma() on the transformed data.

Usage

mllgamma(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 log normal distribution see Loggamma.

Value

mllgamma returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for shapelog and ratelog 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

Hogg, R. V. and Klugman, S. A. (1984), Loss Distributions, Wiley.

Dutang, C., Goulet, V., & Pigeon, M. (2008). actuar: An R package for actuarial science. Journal of Statistical Software, 25(7), 1-37.

See Also

Loggamma for the log normal density.

Examples

mllgamma(precip)

Example output

Maximum likelihood estimates for the Loggamma model 
shapelog   ratelog  
   35.90     10.43  

univariateML documentation built on Jan. 25, 2022, 5:09 p.m.