Computes rescaled excesses over the conditional quantiles

Usage

get_excesses(
  X = NULL,
  y,
  quantiles,
  intermediate_q_feature = FALSE,
  scale_features = FALSE,
  X_scaling = NULL
)

X: A covariate matrix. Can be NULL if there are no covariates.
y: The response variable vector.
quantiles: The intermediate quantiles over which to compute the excesses of y.
intermediate_q_feature: Whether to use the intermediate quantiles as an additional covariate, by appending it to the X matrix (bool).
scale_features: Whether to rescale each input covariates to zero mean and unit variance before applying the network (recommended). If X_scaling is given, X_scaling$scaling overrides scale_features.
X_scaling: Existing "X_scaling" object containing the precomputed mean and variance for each covariate. This enables reusing the scaling choice and parameters from the train set, if computing the excesses on a validation or test set, in order to avoid overfitting. This is performed automatically in the "EQRN" objects.

Named list containing:

Y_excesses: thematrix of response excesses,
X_excesses: the (possibly rescaled and q_feat transformed) covariate matrix,
X_scaling: object of class "X_scaling" to use for consistent scaling on future datasets,
excesses_ratio: and the ratio of escesses for troubleshooting.