mllogis: Logistic distribution maximum likelihood estimation

In univariateML: Maximum Likelihood Estimation for Univariate Densities

Description

Calculates the estimates using nlm with an exponential transform of the location parameter.

Usage

mllogis(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 logistic distribution see Logistic.

Value

mllogis returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for location and scale 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 2, Chapter 23. Wiley, New York.

See Also

Logistic for the Logistic density, nlm for the optimizer this function uses.

Examples

mllogis(precip)

Example output

Maximum likelihood estimates for the Logistic model 
location     scale  
  35.638     7.737  

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