fitLS: Fitting splines with penalized least squares.

In orthogonalsplinebasis: Orthogonal B-Spline Basis Functions

Fitting splines with penalized least squares.

Description

Estimates the control vector for a spline fit by penalized least squares. The penalty being the penalty parameter times the functional inner product of the second derivative of the spline curve.

Usage

fitLS(object, x, y, penalty = 0)

Arguments

object	The SplineBasis object to be used to make the fit
x	predictor variable.
y	response variable.
penalty	The penalty multiplier.

Details

For numeric vector y, and x, and a set of basis functions, represented in object, defined on the knots (k_0,…,k_m). The likelihood is defined by

sum_i (y_i-b(x_i)mu) + integral mu^T b''(t)^T b''(t) mu dt

The function estimates μ.

Value

a vector of the control points.

See Also

SplineBasis

Examples

knots<-c(0,0,0,0:5,5,5,5)
base<-SplineBasis(knots)
x<-seq(0,5,by=.5)
y<-exp(x)+rnorm(length(x),sd=5)
fitLS(base,x,y)

orthogonalsplinebasis documentation built on May 24, 2022, 1:05 a.m.