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 basis. 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. Spencer Graves

[EMAIL PROTECTED] 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?

Kevin

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