ECBC {SBCK}

ECBC (Empirical Copula Bias Correction) method

Description

Perform a multivariate (non stationary) bias correction.

Details

use Schaake shuffle

Super class

SBCK::CDFt -> ECBC

Methods

Public methods

• ECBC$new()

• ECBC$fit()

• ECBC$predict()

• ECBC$clone()

Method new()

Create a new ECBC object.

Usage

ECBC$new(...)

Arguments

...

This class is based to CDFt, and takes the same arguments.

Returns

A new 'ECBC' object.

Method fit()

Fit the bias correction method

Usage

ECBC$fit(Y0, X0, X1)

Arguments

Y0

[matrix: n_samples * n_features] Observations in calibration

X0

[matrix: n_samples * n_features] Model in calibration

X1

[matrix: n_samples * n_features] Model in projection

Returns

NULL

Method predict()

Predict the correction

Usage

ECBC$predict(X1, X0 = NULL)

Arguments

X1

[matrix: n_samples * n_features] Model in projection

X0

[matrix: n_samples * n_features or NULL] Model in calibration

Returns

[matrix or list] Return the matrix of correction of X1 if X0 is NULL, else return a list containing Z1 and Z0, the corrections of X1 and X0

Method clone()

The objects of this class are cloneable with this method.

Usage

ECBC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Vrac, M. and P. Friederichs, 2015: Multivariate—Intervariable, Spatial, and Temporal—Bias Correction. J. Climate, 28, 218–237, https://doi.org/10.1175/JCLI-D-14-00059.1

Examples

## Three bivariate random variables (rnorm and rexp are inverted between ref
## and bias)
XY = SBCK::dataset_gaussian_exp_2d(2000)
X0 = XY$X0 ## Biased in calibration period
Y0 = XY$Y0 ## Reference in calibration period
X1 = XY$X1 ## Biased in projection period


## Bias correction
## Step 1 : construction of the class ECBC
ecbc = SBCK::ECBC$new() 
## Step 2 : Fit the bias correction model
ecbc$fit( Y0 , X0 , X1 )
## Step 3 : perform the bias correction
Z = ecbc$predict(X1,X0)