spencer.graves at pdf.com
Fri Jul 18 02:36:32 CEST 2008
I believe that a short answer to your question is that the
"smooth" is a linear combination of B-spline basis functions, and the
coefficients are the weights assigned to the different B-splines in that
Before offering a much longer answer, I would want to know what
problem you are trying to solve and why you want to know. For a brief
description of B-splines, see "http://en.wikipedia.org/wiki/B-spline".
For a slightly longer commentary on them I suggest the "scripts\ch01.R"
in the DierckxSpline package: That script computes and displays some
B-splines using "splineDesign", "spline.des" in the 'splines' package
plus comparable functions in the 'fda' package. For more info on this,
I found the first chapter of Paul Dierckx (1993) Curve and Surface
Fitting with Splines (Oxford U. Pr.). Beyond that, I've learned a lot
from the 'fda' package and the two companion volumes by Ramsay and
Silverman (2006) Functional Data Analysis, 2nd ed. and (2002) Applied
Functional Data Analysis (both Springer).
If you'd like more help from this listserve, PLEASE do read the
posting guide http://www.R-project.org/posting-guide.html and provide
commented, minimal, self-contained, reproducible code.
Hope this helps.
rkevinburton at charter.net wrote:
> I like what smooth.spline does but I am unclear on the output. I can see from the documentation that there are fit.coef but I am unclear what those coeficients are applied to.With spline I understand the "noraml" coefficients applied to a cubic polynomial. But these coefficients I am not sure how to interpret. If I had a description of the algorithm maybe I could figure it out but as it is I have this question. Any help?
> R-help at r-project.org mailing list
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
More information about the R-help