# mlgamma: Gamma distribution maximum likelihood estimation In univariateML: Maximum Likelihood Estimation for Univariate Densities

## Description

Uses Newton-Raphson to estimate the parameters of the Gamma distribution.

## Usage

 `1` ```mlgamma(x, na.rm = FALSE, ...) ```

## Arguments

 `x` a (non-empty) numeric vector of data values. `na.rm` logical. Should missing values be removed? `...` `rel.tol` is the relative accuracy requested, defaults to `.Machine\$double.eps^0.25`. `iterlim` is a positive integer specifying the maximum number of iterations to be performed before the program is terminated (defaults to `100`).

## Details

For the density function of the Gamma distribution see GammaDist.

## Value

`mlgamma` returns an object of class `univariateML`. This is a named numeric vector with maximum likelihood estimates for `shape` and `rate` 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

Choi, S. C, and R. Wette. "Maximum likelihood estimation of the parameters of the gamma distribution and their bias." Technometrics 11.4 (1969): 683-690.

Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, Volume 1, Chapter 17. Wiley, New York.

## Examples

 `1` ```mlgamma(precip) ```

### Example output

```Maximum likelihood estimates for the Gamma model
shape    rate
4.7171  0.1352
```

univariateML documentation built on Aug. 6, 2020, 1:11 a.m.