update_selection_step: Greedy procedure to determine the selection covariate
In SvenKlaassen/qboost: Quantile Boosting for High-Dimensions
View source: R/update_selection_step.R
update_selection_step R Documentation
Greedy procedure to determine the selection covariate
Description
Calculates the gradient of the smoothed check-loss for the residuals
and selects the column of X which has the highest absolute correlation.
Usage
update_selection_step(X, residuals, tau, h = 0.1, kernel = "Gaussian")
Arguments
X
(matrix)
A matrix of covariates.
residuals
(numeric())
A vector of residuals.
tau
(numeric(1L))
A quantile.
h
(numeric(1L))
The bandwith for smoothing.
kernel
(character(1L))
The kernel for smoothing.
Value
A list with components
-
sel_cov The index of the selected covariates.
-
cor The corresponding correlation.
SvenKlaassen/qboost documentation built on Nov. 8, 2022, 12:13 p.m.
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View source: R/update_selection_step.R
| update_selection_step | R Documentation |
Greedy procedure to determine the selection covariate
Description
Calculates the gradient of the smoothed check-loss for the residuals and selects the column of X which has the highest absolute correlation.
Usage
update_selection_step(X, residuals, tau, h = 0.1, kernel = "Gaussian")
Arguments
X |
( |
residuals |
( |
tau |
( |
h |
( |
kernel |
( |
Value
A list with components
-
sel_covThe index of the selected covariates. -
corThe corresponding correlation.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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