oracle.Bspl: Fit simple nonparametric regression model with cubic...

In gregorkb/spaddinf: Fit two-step estimator for inference in sparse high-dimensional additive models

Fit simple nonparametric regression model with cubic B-splines

Description

Fit simple nonparametric regression model with cubic B-splines

Usage

oracle.Bspl(Y, X, d, plot = FALSE, x = NULL, alpha = 0.05)

Arguments

Y	a response vector (centered)
X	vector of covariate observations
d	the number functions in the cubic B-spline basis
x	a vector of values at which evaluations of the fitted function should be returned/plotted
a	logical indicating whether a plot of the fitted function should be generated

Value

a list containing the fitted function and a vector containing the values of the fitted function at the design points

Examples

data <- data_gen(n = 200, q = 50, r = .9)

oracle.Bspl.out <- oracle.Bspl(Y = data$Y.oracle[,1],
                               X = data$X[,1],
                               d = 10,
                               plot = TRUE,
                               x = NULL)

gregorkb/spaddinf documentation built on Oct. 29, 2024, 3:17 p.m.