Log-normal distribution maximum likelihood estimation

The maximum likelihood estimate of meanlog is the empirical mean of the log-transformed data and the maximum likelihood estimate of sdlog is the square root of the biased sample variance based on the log-transformed data.

Usage

mllnorm(x, na.rm = FALSE, ...)

Arguments

x: a (non-empty) numeric vector of data values.
na.rm: logical. Should missing values be removed?
...: currently affects nothing.

Value

mllnorm returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for meanlog and sdlog 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 Lognormal.

References

Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, Volume 1, Chapter 14. Wiley, New York.

Examples

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