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Nonlinear-Term Configuration for HBSAE Models

Source: R/hbm-config.R
hbm_nonlinear.Rd

Bundles the nonlinear-term arguments of hbm into a single named list. Same opt-in pattern as hbm_control.

Usage

hbm_nonlinear(
  terms,
  type = c("spline", "gp"),
  k = -1L,
  spline_bs = "tp",
  gp_cov = "exp_quad",
  gp_c = NULL,
  gp_scale = NULL
)

Arguments

terms

Character vector of predictor names to be wrapped in s() or gp().

type

Character. "spline" (default) or "gp".

k

Integer. Basis dimension. For splines: passed to mgcv::s(..., k = ...); -1L (default) lets mgcv choose. For GP: passed to brms::gp(..., k = ...) for the Hilbert-space approximate GP (Riutort-Mayol et al.\ 2020); NA = exact GP (slow, not recommended for \(n > 100\)).

spline_bs

Character. Spline basis type ("tp", "cr", "cs", "ps", ...). Default "tp".

gp_cov

Character. GP covariance function: "exp_quad", "matern15", "matern25", "exponential". Default "exp_quad".

gp_c

Numeric or NULL. HSGP boundary-scale factor for brms::gp(c = ...).

gp_scale

Deprecated. Use gp_c instead.

Value

A named list of arguments for hbm, with class c("hbm_config_nonlinear", "hbm_config", "list").

See also

hbm_control, hbm_priors, hbm

Examples

# Penalised regression spline (auto-chosen basis dimension):
nl_spline <- hbm_nonlinear(c("x1", "x3"), type = "spline")

# Spline with cubic-regression basis and fixed k:
nl_cr <- hbm_nonlinear("x1", type = "spline", k = 8, spline_bs = "cr")

# Hilbert-space approximate GP with Matern 5/2 covariance (recommended):
nl_gp <- hbm_nonlinear("x2", type = "gp", k = 20, gp_cov = "matern25")