QDM | R Documentation |
Perform a bias correction.
Mix of delta and quantile method
new()
Create a new QDM object.
QDM$new(delta = "additive", ...)
delta
[character or list] If character : "additive" or "multiplicative". If a list is given, delta[[1]] is the delta transform operator, and delta[[2]] its inverse.
...
[] Named arguments passed to quantile mapping
A new 'QDM' object.
fit()
Fit the bias correction method
QDM$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
QDM$predict(X1, X0 = NULL)
X1
[matrix: n_samples * n_features] Model in projection
X0
[matrix: n_samples * n_features or NULL] Model in calibration
[matrix or list] Return the matrix of correction of X1 if X0 is NULL, else return a list containing Z1 and Z0, the corrections of X1 and X0
clone()
The objects of this class are cloneable with this method.
QDM$clone(deep = FALSE)
deep
Whether to make a deep clone.
Cannon, A. J., Sobie, S. R., and Murdock, T. Q.: Bias correction of simulated precipitation by quantile mapping: how well do methods preserve relative changes in quantiles and extremes?, J. Climate, 28, 6938–6959, https://doi.org/10.1175/JCLI-D-14- 00754.1, 2015.
## 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$X1 ## Biased in projection period
## Bias correction
## Step 1 : construction of the class QDM
qdm = SBCK::QDM$new()
## Step 2 : Fit the bias correction model
qdm$fit( Y0 , X0 , X1 )
## Step 3 : perform the bias correction, Z is a list containing
## corrections
Z = qdm$predict(X1,X0)
Z$Z0 ## Correction in calibration period
Z$Z1 ## Correction in projection period
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