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

mllonorm returns an object of classunivariateML. 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.

mllnorm(precip)