Column-wise MLE of some univariate distributions {Compositional}

Column-wise MLE of some univariate distributions

Description

Column-wise MLE of some univariate distributions.

Usage

colbeta.est(x, tol = 1e-07, maxiters = 100, parallel = FALSE)
collogitnorm.est(x)
colunitweibull.est(x, tol = 1e-07, maxiters = 100, parallel = FALSE)
colzilogitnorm.est(x)

Arguments

x

A numerical matrix with data. Each column refers to a different vector of observations of the same distribution. The values must by percentages, exluding 0 and 1,

tol

The tolerance value to terminate the Newton-Fisher algorithm.

maxiters

The maximum number of iterations to implement.

parallel

Do you want to calculations to take place in parallel? The default value is FALSE

Details

For each column, the same distribution is fitted and its parameters and log-likelihood are computed.

Value

A matrix with two, three or four columns. The first one, two or three columns contain the parameter(s) of the distribution, while the last column contains the relevant log-likelihood.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

N.L. Johnson, S. Kotz & N. Balakrishnan (1994). Continuous Univariate Distributions, Volume 1 (2nd Edition).

N.L. Johnson, S. Kotz & N. Balakrishnan (1970). Distributions in statistics: continuous univariate distributions, Volume 2.

J. Mazucheli, A. F. B. Menezes, L. B. Fernandes, R. P. de Oliveira & M. E. Ghitany (2020). The unit-Weibull distribution as an alternative to the Kumaraswamy distribution for the modeling of quantiles conditional on covariates. Journal of Applied Statistics, DOI:10.1080/02664763.2019.1657813.

See Also

beta.est

Examples

x <- matrix( rbeta(200, 3, 4), ncol = 4 )
a <- colbeta.est(x)