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

Description Usage Arguments Details Value Author(s) References See Also

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

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

`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.

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:

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.
from=sFrechet
- to=Gev.
from=sGumbel
- to=Gev.
from=sWeibull
- to=Gev.