get_excesses: Computes rescaled excesses over the conditional quantiles

In EQRN: Extreme Quantile Regression Neural Networks for Risk Forecasting

Computes rescaled excesses over the conditional quantiles

Description

Computes rescaled excesses over the conditional quantiles

Usage

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

Arguments

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.

Value

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.

EQRN documentation built on April 4, 2025, 12:45 a.m.