# Dist2Dist: Switches from an EV to Another EV Distribution In CompRandFld: Composite-Likelihood Based Analysis of Random Fields

## Description

The function transforms observations belonging to the GEV class from one model to another.

## Usage

 ```1 2``` ```Dist2Dist(data, from='Gev', to='sFrechet', loc=NULL, scale=NULL, shape=NULL) ```

## Arguments

 `data` A numeric vector or a matrix of extreme values. `from` The name of the original extreme value distribution, i.e. `Gev` (the default), see the Details section. `to` The name of the desired extreme value distribution, i.e. `sFrechet` (the default), see the Details section. `loc` A numeric value or vector of location parameters. `scale` A numeric value or vector of scale parameters. `shape` A numeric value or vector of shape parameters.

## Details

If `data` is a numeric vector of length `n` then the dataset is consider as a realisation from an univariate extreme value distribution. Instead, if `data` is a (n x d)-matrix then the columns represent the different variables with extreme value distributions and the rows represent the iid replications. Finally, if `data` is a (d x d x n)-matrix then the columns and rows represent the different variables and the third dimension represents the iid replications.

The parameters `from` and `to` indicate the original extreme value distribution(s) from which the observations are drawn and the target extreme value distribution(s) that the transformed data will follow. The options are:

1. `from=Gev` (generalised extreme value distribution):

• `to=Uniform`, which means uniform distribution;

• `to=sFrechet`, which means standard (or unit) Frechet distribution, that is GEV(1,1,1);

• `to=sGumbel`, which means standard Gumbel distribution, that is GEV(0,1,1);

• `to=sWeibull`, which means standard Weibull distribution, that is GEV(1,1,-1);

• `to=Gev`, which means generalised extreme value distribution. Note, that in this case, it is required to insert vectors of location, scale and shape parameters with dimension `n` in the univariate case, dimension `d` when `data` is (n x d)-matrix and dimension n x d when `data` is (d x d x n)-matrix.

2. `from=sFrechet`

• `to=Gev`.

3. `from=sGumbel`

• `to=Gev`.

4. `from=sWeibull`

• `to=Gev`.

## Value

A numeric vector or matrix of transformed values following the desired distribution.

`FitGev`