ECBC | R Documentation |
Perform a multivariate (non stationary) bias correction.
use Schaake shuffle
SBCK::CDFt
-> ECBC
new()
Create a new ECBC object.
ECBC$new(...)
...
This class is based to CDFt, and takes the same arguments.
A new 'ECBC' object.
fit()
Fit the bias correction method
ECBC$fit(Y0, X0, X1)
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
NULL
predict()
Predict the correction
ECBC$predict(X1, X0 = NULL)
X1
[matrix: n_samples * n_features] Model in projection
X0
[matrix: n_samples * n_features or NULL] Model in calibration
[matrix or list] Return the matrix of correction of X1 if X0 is NULL, else return a list containing Z1 and Z0, the corrections of X1 and X0
clone()
The objects of this class are cloneable with this method.
ECBC$clone(deep = FALSE)
deep
Whether to make a deep clone.
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
## 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)
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