# collognorm.mle: Column-wise MLE of some univariate distributions In Rfast2: A Collection of Efficient and Extremely Fast R Functions II

 Column-wise MLE of some univariate distributions R Documentation

## Column-wise MLE of some univariate distributions

### Description

Column-wise MLE of some univariate distributions.

### Usage

``````collognorm.mle(x)
collogitnorm.mle(x)
colborel.mle(x)
colhalfnorm.mle(x)
colcauchy.mle(x, tol = 1e-07, maxiters = 100, parallel = FALSE)
colbeta.mle(x, tol = 1e-07, maxiters = 100, parallel = FALSE)
colunitweibull.mle(x, tol = 1e-07, maxiters = 100, parallel = FALSE)
colpowerlaw.mle(x)
colsp.mle(x)
colhalfcauchy.mle(x, tol = 1e-07, parallel = FALSE, cores = 0)
colcensweibull.mle(x, di, tol = 1e-07, parallel = FALSE, cores = 0)
colcenspois.mle(x, tol = 1e-07, parallel = FALSE, cores = 0)
``````

### Arguments

 `x` A numerical matrix with data. Each column refers to a different vector of observations of the same distribution. The values of for lognormal must be greater than zero, for the logitnormal, beta, unit Weibull and sp they must be numbers between 0 and 1, exluding 0 and 1, whereas for the Borel distribution the x must contain integer values greater than 1. For the halfnormal and powerlaw the numbers must be strictly positive, while for the ordinal this can be a numerical matrix with values 1, 2, 3,..., not zeros. The censored Poisson (colcenspois.mle) requires discrete data (counts). `di` A vector of 0s (censored) and 1s (not censored) vales. `link` This can either be "logit" or "probit". It is the link function to be used. `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 `cores` In case you set parallel = TRUE, then you need to specify the number of cores.

### Details

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

### Value

A matrix with two or three columns. The first one or the first two contain the parameter(s) of the distribution and the second or third column the relevant log-likelihood. For the colordinal.mle() a list including:

 `param` A matrix with the intercepts (threshold coefficients) of the model applied to each column (or variable). `loglik` The log-likelihood values.

### Author(s)

Michail Tsagris and Stefanos Fafalios.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Stefanos Fafalios stefanosfafalios@gmail.com.

### References

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

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

Agresti A. (2002) Categorical Data. Second edition. Wiley.

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, 47(6): 954–974.

```censpois.mle, gammapois.mle, powerlaw.mle, unitweibull.mle ```
``````x <- matrix( exp( rnorm(500 * 50) ), ncol = 50)