Description Usage Arguments Value Author(s) References See Also Examples
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.
1 2 3 4 | 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)
|
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. |
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 |
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. |
Steven Spiriti, Philip Smith, and Pierre Lecuyer
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.
fit.search.numknots
for the case where the number of knots is not specified in advance.
1 2 3 4 5 6 | 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)
|
Loading required package: splines
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