mlinvgauss: Inverse Gaussian (Wald) maximum likelihood estimation

In univariateML: Maximum Likelihood Estimation for Univariate Densities

Description

The maximum likelihood estimate of mean is the empirical mean and the maximum likelihood estimate of 1/shape is the difference between the mean of reciprocals and the reciprocal of the mean.

Usage

mlinvgauss(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 Inverse Gamma distribution see InverseGaussian.

Value

mlinvgauss returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for mean and shape 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 15. Wiley, New York.

See Also

InverseGaussian for the Inverse Gaussian density.

Examples

mlinvgauss(precip)

Example output

Maximum likelihood estimates for the Inverse Gaussian model 
  mean   shape  
 34.89  107.48  

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