SBCK (version 1.0.0)

RBC: RBC (Random Bias Correction) method

Description

Perform a multivariate bias correction of X with respect to Y randomly.

Methods

Public methods

• RBC$new()

• RBC$fit()

• RBC$predict()

• RBC$clone()

---

Method new()

Create a new RBC object.

Usage

RBC$new()

Returns

A new `RBC` object.

---

Method fit()

Fit the bias correction method

Usage

RBC$fit(Y0, X0, X1 = NULL)

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, can be NULL for stationary BC method

Returns

NULL

---

Method predict()

Predict the correction. Use named keywords to use stationary or non-stationary method.

Usage

RBC$predict(X1 = NULL, X0 = NULL)

Arguments

X1

[matrix: n_samples * n_features or NULL] 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

RBC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

Only for comparison.

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 RBC
rbc = SBCK::RBC$new() 
## Step 2 : Fit the bias correction model
rbc$fit( Y0 , X0 , X1 )
## Step 3 : perform the bias correction
Z = rbc$predict(X1,X0) 
## Z$Z0 # BC of X0
## Z$Z1 # BC of X1