Log-logistic distribution maximum likelihood estimation

The maximum likelihood estimate of shape and rate are calculated by transforming the data back to the logistic model and applying mllogis

mlllogis(x, na.rm = FALSE)

Arguments

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

Value

mlllogis 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 my match.call

Details

For the density function of the log-logistic distribution see Loglogistic

References

Kleiber, C. and Kotz, S. (2003), Statistical Size Distributions in Economics and Actuarial Sciences, Wiley.

Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2012), Loss Models, From Data to Decisions, Fourth Edition, 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

Loglogistic for the log-logistic density.

Examples

mllnorm(precip)
#> Maximum likelihood estimates for the Lognormal model 
#> meanlog    sdlog  
#>  3.4424   0.5247