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beta.est

MLE of distributions defined in the (0, 1) interval


Description

MLE of distributions defined in the (0, 1) interval.

Usage

beta.est(x, tol = 1e-07)
ibeta.est(x, tol = 1e-07)
logitnorm.est(x)
hsecant01.est(x, tol = 1e-07)
simplex.est(x, tol = 1e-07)
kumar.est(x, tol = 1e-07)
zilogitnorm.est(x)

Arguments

x

A numerical vector with proportions, i.e. numbers in (0, 1) (zeros and ones are not allowed).

tol

The tolerance level up to which the maximisation stops.

Details

Maximum likelihood estimation of the parameters of the beta distribution is performed via Newton-Raphson. The distributions and hence the functions does not accept zeros. "logitnorm.mle" fits the logistic normal, hence no nwewton-Raphson is required and the "hypersecant01.mle" and "simplex.est" use the golden ratio search as is it faster than the Newton-Raphson (less computations). The "zilogitnorm.est" stands for the zero inflated logistic normal distribution.

Value

A list including:

iters

The number of iterations required by the Newton-Raphson.

loglik

The value of the log-likelihood.

param

The estimated parameters. In the case of "hypersecant01.est" this is called "theta" as there is only one parameter.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Manos Papadakis <papadakm95@gmail.com>

References

Kumaraswamy, P. (1980). A generalized probability density function for double-bounded random processes. Journal of Hydrology. 46 (1-2): 79-88.

Jones, M.C. (2009). Kumaraswamy's distribution: A beta-type distribution with some tractability advantages. Statistical Methodology. 6(1): 70-81.

Zhang, W. & Wei, H. (2008). Maximum likelihood estimation for simplex distribution nonlinear mixed models via the stochastic approximation algorithm. The Rocky Mountain Journal of Mathematics, 38(5): 1863-1875.

You can also check the relevant wikipedia pages.

See Also

Examples

x <- rbeta(1000, 1, 4)
beta.est(x)
ibeta.est(x)

x <- runif(1000)
hsecant01.est(x)
logitnorm.est(x)
ibeta.est(x)

x <- rbeta(1000, 2, 5)
x[sample(1:1000, 50)] <- 0
ibeta.est(x)

Compositional

Compositional Data Analysis

v4.6
GPL (>= 2)
Authors
Michail Tsagris [aut, cre], Giorgos Athineou [aut], Abdulaziz Alenazi [ctb]
Initial release
2021-04-27

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