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.
mllnorm(x, na.rm = FALSE)
x | a (non-empty) numeric vector of data values. |
---|---|
na.rm | logical. Should missing values be removed? |
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
For the density function of the log normal distribution see Lognormal.
Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, Volume 1, Chapter 14. Wiley, New York.
Lognormal for the log normal density.
mllnorm(precip)#> Maximum likelihood estimates for the Lognormal model #> meanlog sdlog #> 3.4424 0.5247