R2D2 {SBCK}

R2D2 (Rank Resampling for Distributions and Dependences) method

Description

Perform a multivariate (non stationary) bias correction.

Details

Use rankshuffle in calibration and projection period with CDFt

Super class

SBCK::CDFt -> R2D2

Public fields

irefs

[vector of int] Indexes for shuffle. Defaults is base::c(1)

Methods

Public methods

• R2D2$new()

• R2D2$fit()

• R2D2$predict()

• R2D2$clone()

Method new()

Create a new R2D2 object.

Usage

R2D2$new(irefs = base::c(1), ...)

Arguments

irefs

[vector of int] Indexes for shuffle. Defaults is base::c(1) model

...

[] all others arguments are passed to CDFt class.

Returns

A new 'R2D2' object.

Method fit()

Fit the bias correction method

Usage

R2D2$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

R2D2$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

R2D2$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Vrac, M.: Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R2 D2 ) bias correction, Hydrol. Earth Syst. Sci., 22, 3175–3196, https://doi.org/10.5194/hess-22-3175-2018, 2018.

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 R2D2 
r2d2 = SBCK::R2D2$new() 
## Step 2 : Fit the bias correction model
r2d2$fit( Y0 , X0 , X1 )
## Step 3 : perform the bias correction
Z = r2d2$predict(X1,X0)