mlexp: Exponential distribution maximum likelihood estimation

In univariateML: Maximum Likelihood Estimation for Univariate Densities

Description

The maximum likelihood estimate of rate is the inverse sample mean.

Usage

mlexp(x, na.rm = FALSE, ...)

Arguments

x	a (non-empty) numeric vector of data values.
na.rm	logical. Should missing values be removed? If FALSE, the function fails when x contains missing values.
...	currently affects nothing.

Details

For the density function of the exponential distribution see Exponential.

Value

mlexp returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for rate 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 19. Wiley, New York.

See Also

Exponential for the exponential density.

Examples

mlexp(precip)

Example output

Maximum likelihood estimates for the Exponential model 
   rate  
0.02867  

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