SBCK (version 1.0.0)

MRec: MRec (Matrix Recorrelation) method

Description

Perform a multivariate bias correction with Gaussian assumption.

Public fields

n_features

[integer] Numbers of features

Methods

Public methods

• MRec$new()

• MRec$fit()

• MRec$predict()

• MRec$clone()

---

Method new()

Create a new MRec object.

Usage

MRec$new(distY = NULL, distX = NULL)

Arguments

distY

[A list of ROOPSD distribution or NULL] Describe the law of each margins. A list permit to use different laws for each margins. Default is empirical.

distX

[A list of ROOPSD distribution or NULL] Describe the law of each margins. A list permit to use different laws for each margins. Default is empirical.

Returns

A new `MRec` object.

---

Method fit()

Fit the bias correction method

Usage

MRec$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

MRec$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

MRec$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

Only pearson correlations are corrected.

References

Bárdossy, A. and Pegram, G.: Multiscale spatial recorrelation of RCM precipitation to produce unbiased climate change scenarios over large areas and small, Water Resources Research, 48, 9502–, https://doi.org/10.1029/2011WR011524, 2012.

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 MRec 
mrec = SBCK::MRec$new() 
## Step 2 : Fit the bias correction model
mrec$fit( Y0 , X0 , X1 )
## Step 3 : perform the bias correction, Z is a list containing corrections.
Z = mrec$predict(X1,X0) ## X0 is optional, in this case Z0 is NULL
Z$Z0 ## Correction in calibration period
Z$Z1 ## Correction in projection period