View source: R/positive_mle.R View source: R/gammamle.R

MLE of continuous univariate distributions defined on the positive line | R Documentation |

MLE of continuous univariate distributions defined on the positive line.

gammamle(x, tol = 1e-09) chisq.mle(x, tol = 1e-09) weibull.mle(x, tol = 1e-09, maxiters = 100) lomax.mle(x, tol = 1e-09) foldnorm.mle(x, tol = 1e-09) betaprime.mle(x, tol = 1e-09) logcauchy.mle(x, tol = 1e-09) loglogistic.mle(x, tol = 1e-09) halfnorm.mle(x) invgauss.mle(x) lognorm.mle(x) pareto.mle(x) expmle(x) exp2.mle(x) maxboltz.mle(x) rayleigh.mle(x) normlog.mle(x) lindley.mle(x)

`x` |
A vector with positive valued data (zeros are not allowed). |

`tol` |
The tolerance level up to which the maximisation stops; set to 1e-09 by default. |

`maxiters` |
The maximum number of iterations the Newton-Raphson will perform. |

Instead of maximising the log-likelihood via a numerical optimiser we have used a Newton-Raphson algorithm which is faster. See wikipedia for the equations to be solved. For the t distribution we need the degrees of freedom and estimate the location and scatter parameters. If you want to to fit an inverse gamma distribution simply do "gamma.mle(1/x)". The log-likelihood and the parameters are for the inverse gamma.

The "normlog.mle" is simply the normal distribution where all values are positive. Note, this is not log-normal. It is the normal with a log link. Similarly to the inverse gaussian distribution where the mean is an exponentiated. This comes from the GLM theory.

Usually a list with three elements, but this is not for all cases.

`iters` |
The number of iterations required for the Newton-Raphson to converge. |

`loglik` |
The value of the maximised log-likelihood. |

`param` |
The vector of the parameters. |

Michail Tsagris

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

Kalimuthu Krishnamoorthy, Meesook Lee and Wang Xiao (2015). Likelihood ratio tests for comparing several gamma distributions. Environmetrics, 26(8):571-583.

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

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

Tsagris M., Beneki C. and Hassani H. (2014). On the folded normal distribution. Mathematics, 2(1):12-28.

Sharma V. K., Singh S. K., Singh U. and Agiwal V. (2015). The inverse Lindley distribution: a stress-strength reliability model with application to head and neck cancer data. Journal of Industrial and Production Engineering, 32(3): 162-173.

You can also check the relevant wikipedia pages for these distributions.

```
zip.mle, normal.mle, beta.mle
```

x <- rgamma(100, 3, 4) system.time( for (i in 1:20) gammamle(x) ) ## system.time( for (i in 1:20) fitdistr(x,"gamma") ) a <- glm(x ~ 1, gaussian(log) ) res<-normlog.mle(x)

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