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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.

See also

Lognormal for the log normal density.

Examples

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