freeknotfit: Fit Free-Knot Splines To Data

In freeknotsplines: Algorithms for Implementing Free-Knot Splines

Description

These functions fit free-knot splines to data with one independent variable and one dependent variable. It is assumed that the number of knots is known in advance. freelsgen and freelsgold fit least-squares splines with no penalty, while freepsgen and freepsgold fit penalized splines. freelsgen and freepsgen use a genetic algorithm, while freelsgold and freepsgold use a blind search augmented with a golden section algorithm.

Usage

freelsgen(x, y, degree, numknot, seed = 5, stream = 0)
freelsgold(x, y, degree, numknot, seed = 5, stream = 0)
freepsgen(x, y, degree, numknot, seed = 5, stream = 0)
freepsgold(x, y, degree, numknot, seed = 5, stream = 0)

Arguments

x	A vector containing the values of the independent variable.
y	A vector containing the values of the dependent variable.
degree	The degree of the spline fit.
numknot	The number of knots.
seed	The value of the initial seed. Defaults to 5.
stream	The value of the initial stream to be used for parallel programming. Defaults to 0.

Value

An object of class "freekt" containing the following components:

x	A vector containing the x values.
y	A vector containing the y values.
degree	The degree of the spline fit.
seed	The value of the initial seed.
stream	The value of the stream.
lambda	The optimum amount of penalty. This is automatically equal to 0 for freelsgen and freelsgold.
optknot	A vector containing the optimal knots.
tracehat	The trace of the hat matrix for the optimal fit.
GCV	The value of generalized cross validation (GCV) for the optimal fit.
GSJS	The GSJS estimator, an estimator of the variance of the data.
call	The function call.

Author(s)

Steven Spiriti, Philip Smith, and Pierre Lecuyer

References

Eubank, R. (1999), Nonparametric Regression and Spline Smoothing, New York: Marcel Dekker, Inc., Second ed.

Spiriti, S., Eubank, R., Smith, P., Young, D., "Knot Selection for Least-Squares and Penalized Splines," Journal of Statistical Computation and Simulation, in press.

See Also

fit.search.numknots for the case where the number of knots is not specified in advance.

Examples

x <- 0:30/30
truey <- x*sin(10*x)
set.seed(10556)
y <- truey + rnorm(31, 0, 0.2)
xy.freekt <- freelsgen(x, y, degree = 2, numknot = 2, 555)
plot.freekt(xy.freekt, xfit = 0:1000/1000)

Example output

Loading required package: splines

freeknotsplines documentation built on May 2, 2019, 8:51 a.m.