# fit.davies.p: Fits and plots Davies distributions to datasets In Davies: The Davies Quantile Function

## Description

A newbie wrapper (and pretty-printer) for `maximum.likelihood()` and `least.squares()`. Draws an empirical quantile function (`fit.davies.p()`) or PDF (`fit.davies.q()`) and the dataset

## Usage

 ```1 2``` ```fit.davies.p(x , print.fit=FALSE, use.q=TRUE , params=NULL, small=1e-5 , ...) fit.davies.q(x , print.fit=FALSE, use.q=TRUE , params=NULL, ...) ```

## Arguments

 `x` dataset to be fitted and plotted `print.fit` Boolean with `TRUE` meaning print details of the fit `use.q` Boolean with `TRUE` meaning use `least.squares()` (rather than `maximum.likelihood()`) `params` three-element vector holding the three parameters of the davies dataset. If `NULL`, determine the parameters using the method indicated by `use.q` `small` small positive number showing range of quantiles to plot `...` Additional parameters passed to `plot()`

## Value

If `print.fit` is `TRUE`, return the optimal parameters

## Author(s)

Robin K. S. Hankin

`least.squares` , `maximum.likelihood`

## Examples

 ```1 2 3 4 5``` ``` fit.davies.q(rchisq(100,1)) fit.davies.p(exp(rnorm(100))) data(x00m700p4) fit.davies.q(x00m700p4) ```

### Example output

```
```

Davies documentation built on May 29, 2017, 3:20 p.m.