mllogitnorm: Logit-Normal distribution maximum likelihood estimation

In JonasMoss/univariateML: Maximum Likelihood Estimation for Univariate Densities

Logit-Normal distribution maximum likelihood estimation

Description

The maximum likelihood estimate of mu is the empirical mean of the logit transformed data and the maximum likelihood estimate of sigma is the square root of the logit transformed biased sample variance.

Usage

mllogitnorm(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 logit-normal distribution see dlogitnorm.

Value

mllogitnorm returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for mu and sigma 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

Atchison, J., & Shen, S. M. (1980). Logistic-normal distributions: Some properties and uses. Biometrika, 67(2), 261-272.

See Also

link[dlogitnorm]dlogitnormfor the normal density.

Examples

AIC(mllogitnorm(USArrests$Rape / 100))

JonasMoss/univariateML documentation built on Feb. 6, 2024, 2:21 p.m.