basis_of_degree: Compute Degree of Basis Functions
In hal9001: The Scalable Highly Adaptive Lasso

basis_of_degree

R Documentation

Compute Degree of Basis Functions

Find the full list of basis functions up to a particular degree

basis_of_degree(
  x,
  degree,
  smoothness_orders,
  include_zero_order,
  include_lower_order
)

`x`	An input `matrix` containing observations and covariates following standard conventions in problems of statistical learning.
`degree`	The highest order of interaction terms for which the basis functions ought to be generated. The default (`NULL`) corresponds to generating basis functions for the full dimensionality of the input matrix.
`smoothness_orders`	An integer vector of length `ncol(x)` specifying the desired smoothness of the function in each covariate. k = 0 is no smoothness (indicator basis), k = 1 is first order smoothness, and so on. For an additive model, the component function for each covariate will have the degree of smoothness as specified by smoothness_orders. For non-additive components (tensor products of univariate basis functions), the univariate basis functions in each tensor product have smoothness degree as specified by smoothness_orders.
`include_zero_order`	A `logical`, indicating whether the zeroth order basis functions are included for each covariate (if `TRUE`), in addition to the smooth basis functions given by `smoothness_orders`. This allows the algorithm to data-adaptively choose the appropriate degree of smoothness.
`include_lower_order`	A `logical`, like `include_zero_order`, except including all basis functions of lower smoothness degrees than specified via `smoothness_orders`.