Fair enough. FOr a spline interpolation I can do the following: > n <- 9 > x <- 1:n > y <- rnorm(n) > plot(x, y, main = paste("spline[fun](.) through", n, "points")) > lines(spline(x, y))
Then look at the coefficients generated as: > f <- splinefun(x, y) > ls(envir = environment(f)) [1] "ties" "ux" "z" > splinecoef <- get("z", envir = environment(f)) > slinecoef $method [1] 3 $n [1] 9 $x [1] 1 2 3 4 5 6 7 8 9 $y [1] 0.93571604 0.44240485 0.45451903 -0.96207396 -1.13246522 -0.60032698 [7] -1.77506105 -0.09171419 -0.23262573 $b [1] -1.53673409 0.22775629 -0.81788209 -1.16966436 0.73558677 -0.68744178 [7] 0.08639287 1.86770869 -2.92992167 $c [1] 1.3657783 0.3987121 -1.4443504 1.0925682 0.8126830 -2.2357115 3.0095462 [8] -1.2282303 -3.5694000 $d [1] -0.32235542 -0.61435416 0.84563953 -0.09329507 -1.01613149 1.74841922 [7] -1.41259217 -0.78038989 -0.78038989 WHen I look at ?spline there is even an example of "manually" using these coefficeients: ## Manual spline evaluation --- demo the coefficients : .x <- get("ux", envir = environment(f)) u <- seq(3,6, by = 0.25) (ii <- findInterval(u, .x)) dx <- u - .x[ii] f.u <- with(splinecoef, y[ii] + dx*(b[ii] + dx*(c[ii] + dx* d[ii]))) stopifnot(all.equal(f(u), f.u)) For the smooth.spline as spl <- smooth.spline(x,y) I can also look at the coefficients: spl$fit $knot [1] 0.000 0.000 0.000 0.000 0.125 0.250 0.375 0.500 0.625 0.750 0.875 1.000 [13] 1.000 1.000 1.000 $nk [1] 11 $min [1] 1 $range [1] 8 $coef [1] 0.90345898 0.73823276 0.40777431 -0.08046715 -0.54625461 -0.85205147 [7] -0.96233408 -0.91373830 -0.66529714 -0.47674774 -0.38246971 attr(,"class") [1] "smooth.spline.fit" But there isn't an example on how to "manual" use these coefficients. This is what I was asking about. Once I hae the coefficients how do I "manually" interpolate using the coefficients given and x. Thank you. Kevin ---- Spencer Graves <[EMAIL PROTECTED]> wrote: > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html and provide commented, > minimal, self-contained, reproducible code. > > I do NOT know how to do what you want, but with a self-contained > example, I suspect many people on this list -- probably including me -- > could easily solve the problem. Without such an example, there is a > high probability that any answer might (a) not respond to your need, and > (b) take more time to develop, just because we don't know enough of what > you are asking. > > Spencer > > [EMAIL PROTECTED] wrote: > > Like I indicated. I understand the coefficients in a B-spline context. If I > > use the the 'spline' or 'splinefun' I can get the coefficients and they are > > grouped as 'a', 'b', 'c', and 'd' coefficients. But the coefficients for > > smooth.spline is just an array. I basically want to take these coefficients > > and outside of 'R' use them to form an interpolation. In other words I want > > 'R' to do the hard work and then export the results so they can be used > > else where. > > > > Thank you. > > > > Kevin > > > > Spencer Graves wrote: > > 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 > >> > >> ______________________________________________ > >> R-help@r-project.org mailing list > >> https://stat.ethz.ch/mailman/listinfo/r-help > >> PLEASE do read the posting guide > >> http://www.R-project.org/posting-guide.html > >> and provide commented, minimal, self-contained, reproducible code. > >> > > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.