Perform a multivariate bias correction of X with respect to Y
Dependence is corrected with multi_schaake_shuffle.
[vector of int] Indexes for shuffle. Defaults is base::c(1)
Create a new QMrs object.
QMrs$new(irefs = base::c(1), ...)
[vector of int] Indexes for shuffle. Defaults is base::c(1) model
 all others arguments are passed to QM class.
A new 'QMrs' object.
Fit the bias correction method
[matrix: n_samples * n_features] Observations in calibration
[matrix: n_samples * n_features] Model in calibration
Predict the correction
[matrix: n_samples * n_features or NULL] Model in calibration
[matrix] Return the corrections of X0
The objects of this class are cloneable with this method.
QMrs$clone(deep = FALSE)
Whether to make a deep clone.
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.
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## 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)
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