sb_normalize: Core helpers for SelectBoost-style beta regression

Description

These helpers expose the individual stages of the SelectBoost workflow so that beta-regression selectors can be combined with correlation-aware resampling directly from SelectBoost.beta. They normalise the design matrix, derive correlation structures, form groups of correlated predictors, generate Gaussian surrogates that mimic the observed dependency structure, and apply a user-provided selector on each resampled design.

Usage

sb_normalize(X, center = NULL, scale = NULL, eps = 1e-08)
sb_compute_corr(X, corrfunc = "cor")
sb_group_variables(corr_mat, c0)

Value

sb_normalize() returns a centred, \(\ell_2\)-scaled copy of X.

sb_compute_corr() returns the association matrix.

sb_group_variables() returns a list of integer vectors, one per variable, describing the correlated group it belongs to.

Arguments

X: Numeric matrix of predictors.
center: Optional centering vector recycled to the number of columns. Defaults to the column means of X.
scale: Optional scaling vector recycled to the number of columns. Defaults to the column-wise \(\ell_2\) norms of the centred matrix.
eps: Small positive constant used when normalising columns.
corrfunc: Function or character string used to compute pairwise associations. Defaults to "cor".
corr_mat: Numeric matrix of associations.
c0: Threshold applied to the absolute correlations.

Examples

Run this code

sb_normalize(matrix(rnorm(20), 5))

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