RBC | R Documentation |
Perform a multivariate bias correction of X with respect to Y randomly.
Only for comparison.
new()
Create a new RBC object.
RBC$new()
A new 'RBC' object.
fit()
Fit the bias correction method
RBC$fit(Y0, X0, X1 = NULL)
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
NULL
predict()
Predict the correction. Use named keywords to use stationary or non-stationary method.
RBC$predict(X1 = NULL, X0 = NULL)
X1
[matrix: n_samples * n_features or NULL] Model in projection
X0
[matrix: n_samples * n_features or NULL] Model in calibration
[matrix or list] Return the matrix of correction of X1 if X0 is NULL, else return a list containing Z1 and Z0, the corrections of X1 and X0
clone()
The objects of this class are cloneable with this method.
RBC$clone(deep = FALSE)
deep
Whether to make a deep clone.
## 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
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