Log-gamma distribution maximum likelihood estimation

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.

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

Details

For the density function of the log normal distribution see Loggamma.

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.

Examples

mllgamma(precip)
#> Maximum likelihood estimates for the Loggamma model 
#> shapelog   ratelog  
#>    35.90     10.43