QMrs {SBCK}

Quantile Mapping RankShuffle method

Description

Perform a multivariate bias correction of X with respect to Y

Details

Dependence is corrected with multi_schaake_shuffle.

Super class

SBCK::QM -> QMrs

Public fields

irefs

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

Methods

Public methods

• QMrs$new()

• QMrs$fit()

• QMrs$predict()

• QMrs$clone()

Method new()

Create a new QMrs object.

Usage

QMrs$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 QM class.

Returns

A new 'QMrs' object.

Method fit()

Fit the bias correction method

Usage

QMrs$fit(Y0, X0)

Arguments

Y0

[matrix: n_samples * n_features] Observations in calibration

X0

[matrix: n_samples * n_features] Model in calibration

Returns

NULL

Method predict()

Predict the correction

Usage

QMrs$predict(X0)

Arguments

X0

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

Returns

[matrix] Return the corrections of X0

Method clone()

The objects of this class are cloneable with this method.

Usage

QMrs$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

## Bias correction
## Step 1 : construction of the class QMrs 
qmrs = SBCK::QMrs$new() 
## Step 2 : Fit the bias correction model
qmrs$fit( Y0 , X0 )
## Step 3 : perform the bias correction
Z0 = qmrs$predict(X0)