Log-gamma distribution maximum likelihood estimation

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

mllgamma(x, na.rm = FALSE, rel.tol = .Machine$double.eps^0.25,
  iterlim = 100)

Arguments

x	a (non-empty) numeric vector of data values.
na.rm	logical. Should missing values be removed?
rel.tol	Relative accuracy; passed to `mlgamma`.
iterlim	A positive integer specifying the maximum number of iterations to be performed before the program is terminated. 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