mllnorm: Log-normal distribution maximum likelihood estimation

In univariateML: Maximum Likelihood Estimation for Univariate Densities

Description

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.

Details

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

Value

mllonorm 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

References

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

See Also

Lognormal for the log normal density.

Examples

mllnorm(precip)

Example output

Maximum likelihood estimates for the Lognormal model 
meanlog    sdlog  
 3.4424   0.5247  

univariateML documentation built on Jan. 25, 2022, 5:09 p.m.