Extract coefficients from a psvr_mape model

Usage

# S3 method for class 'psvr_mape'
coef(object, ...)

Arguments

object: An object of class "psvr_mape".
...: Ignored.

Value

A named list with five components:

alpha, alpha_star: The length-N pre-pruning dual variables $\alpha_k$ and $\alpha^*_k$.
beta: The pruned dual differences $\beta_k = \alpha_k - \alpha^*_k$ over the support-vector indices (length n_sv); this is what predict() uses.
b: Bias term.
support_data: Support vector input matrix.

The LS-SVR classes return three components rather than five, since they have no alpha_star and no pruned beta; the absent components are not materialised as NULL. So names(coef(fit)) depends on the model family. That is deliberate: each class is family-specific, and inventing empty slots to make the two agree would add structure with nothing to inherit it from.

Renamed in 0.0.2.9011

alpha previously held the pruned $\beta$ and support_data was named X_sv, which made coef(fit)$alpha mean the length-n_sv $\beta$ here but the length-N dual $\alpha$ on a fit from the superseded psvr(): one generic returning two different vectors under one name, silently, depending on entry point. The $\beta$-under-alpha meaning is the one 0.0.2.9004 moved away from on the object itself; this aligns coef() with it.