np: Creates design matrices for univariate and bivariate...

In bayesGAM: Fit Multivariate Response Generalized Additive Models using Hamiltonian Monte Carlo

Creates design matrices for univariate and bivariate applications

Description

np accepts one or two numeric vectors of equal length as inputs. From these inputs, univariate or bivariate smoothing design matrices are produced. Currently available basis functions are truncated polynomials and thin plate splines. When bivariate smoothing is selected, np calls create_bivariate_design.

Usage

np(x1, x2 = NULL, num_knots = NULL, knots = NULL, basis = "tps", degree = 3)

Arguments

x1	numeric vector
x2	optional vector for bivariate non-parametric function
num_knots	optional number of knots
knots	optional numeric vector of knots
basis	character vector for basis function. tps for thin-plate spline and trunc.poly for truncated polynomial
degree	for truncated polynomial basis function

Value

list with the following elements:

• X parametric design matrix

• Z non-parametric design matrix

• knots numeric vector of knots for the model

• Xnms names of parameters passed to np

• basis selected basis function

• degree degree for truncated polynomial basis function

References

Ruppert, David, Matt P. Wand, and Raymond J. Carroll. Semiparametric Regression. No. 12. Cambridge university press, 2003. Section 5.6.

Matt Wand (2018). SemiPar: Semiparametric Regression. R package version 1.0-4.2.

Examples

x1 <- rnorm(100)
res <- np(x1, num_knots=10, basis="trunc.poly", degree=2)
res

bayesGAM documentation built on March 18, 2022, 6:29 p.m.