similar to: Derivative of a splinefun function.

This is a low priority bug that has been around for a while, but I came acr= oss it again while alpha testing 2.8. The resulting function for splinefun gives incorrect deriv values when x is= less than the smallest x-value used to create the function (at least in on= e circumstance), but does the correct thing for x greater than the largest = x-value. Here is an example: > x <- 1:10 >

Has anyone got a function for smooth monotonic interpolation of a univariate function? I'm after something like the NAG function PCHIM which does monotonic Hermite interpolation. Alternatively, montononic cubic spline interpolation. Please reply directly. Rob Hyndman ___________________________________________________ Rob J Hyndman Associate Professor & Director of Consulting

Dear All, I tried following for getting derivative of a polynomial in R i<- -10:10 x<-i*i*i+3*i*i+2 fun_spline<-splinefun(i,x) plot(x,type="l") lines(x,fx_spline(x, deriv=1), col='green') lines(x,fx_spline(x, deriv=2), col='green') Now when I plot 3*i*i + 6*i and 6*i + 6 the plot was not same for first deivative. where as the 2nd derivative was same Is this a

--MP_/Rxf/JAvsQx5JLkhZFc9Jmn4 Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: 7bit Content-Disposition: inline G'day Greg, On Mon, 6 Oct 2008 19:50:13 +0200 (CEST) Greg.Snow at imail.org wrote: > This is a low priority bug that has been around for a while, but I > came across it again while alpha testing 2.8. Indeed, that bug must have been around since

At present R has separate functions "spline" and "splinefun". The first of these carries out spline interpolation of a data set and returns the interpolated values; the second returns the interpolating function itself (approx and approxfun are similar). I would like to combine these into a single function "spline" with an (optional) argument which determines which

I am working with the function: splinefun() ... When plugging in the variables, I get the function program as if though having only entered '"splinefun". only way to get the values is by spline(xxx,yyy, n=length(xxx)/10, ties = mean)$x and spline(xxx,yyy, n=length(xxx)/10, ties = mean)$y. I'm just wondering if there is something wrong with the package or if I'm doing

--MP_/kvy20nVajVG/n.8m=_ZjLAX Content-Type: text/plain; charset=US-ASCII Content-Transfer-Encoding: 7bit Content-Disposition: inline On Tue, 7 Oct 2008 19:31:03 +0800 Berwin A Turlach <berwin at maths.uwa.edu.au> wrote: > The attached patch (against the current SVN version of R) implements > the latter strategy. With this patch applied, "make check > FORCE=FORCE" passes

Hi, In my version of R, the stats package splinefun code for fitting a Fritsch and Carlson monotonic spline does not appear to guarantee a monotonic result. If two adjoining sections both have over/undershoot the way the resulting adjustment of alpha and beta is performed can give modified values which still do not satisfy the required constraints. I do not think this is due to finite precision

hello, is it possible to do something like splinefun(x,y), but with a 3rd dim? for example, if i have a 2-dim system like: x<-1:100 y<-rexp(100,1) func<-splinefun(x,sort(y)) func(n) # returns interpolated value of y (after sorting) given x=n # i can check this by: plot(x,sort(y)) lines(spline(x,sort(y))) Can i do the same thing with an x,y, and z? i have found the akima

I want to fit a cubic spline of x on y. where : x [1] 467 468 460 460 450 432 419 420 423 423 y [1] 1 2 3 4 5 6 7 8 9 10 using the syntax spline(y, x) I got following output : $x [1] 1.000000 1.310345 1.620690 1.931034 2.241379 2.551724 2.862069 [8] 3.172414 3.482759 3.793103 4.103448 4.413793 4.724138 5.034483 [15] 5.344828 5.655172

Readers, A data set comprises A B C 10 6 .2 20 7 .4 30 8 .16 40 9 .0256 My requirement is to obtain the derivative for values of A with respect to B, create a function in R and plot this derivative against another variable (e.g. values in column C). Have searched the mailing list and found reference to a function 'D', but the help accessed via '?D' and '?deriv' does not

BUG IN SPLINE()? Version R-1.0.1, system i486,linux If the spline(x,y,method="natural") function is given values outside the range of the data, it does not give a warning. Moreover, the extrapolated value reported is not the ordinate of the natural spline defined by (x,y). Example. Let x <- c(2,5,8,10) and y <- c(1.2266,-1.7606,-0.5051,1.0390). Then interpolate/extrapolate with

Hello, I come from a non statistics background, but R is available to me, and I needed to test an implementation of smoothing spline that I have written in c++, so I would like to match the results with R (for my unit tests) I am following http://www.nabble.com/file/p25569553/SPLINES.PDF SPLINES.PDF where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) +

Hello, I am struggling to overcome following problem: I have matrix Vf and matrix V, which both have 3 columns and I want to create a spline function basing on coordinates from this matrices (more precisely coordinates of function nr 1: x are taken from Vf[,1] and y are taken from V[,1] respectively), because function apply() base on 1 argument I made it in this way:

--AZaLVt6Pw+ Content-Type: text/plain; charset=us-ascii Content-Description: message body text Content-Transfer-Encoding: 7bit Dear all, I was looking at "splinefun" and the underlying C code and believe that there is a memory access error in the C routine "spline_eval". Specifically, on line 368 and following the following code appears: if(ul < x[i] || x[i+1] < ul)

Hello R help I have been trying to use Rob Hyndman's monotonically increasing spline function. But like another user or two seem have a problem with a missing DLL (namely "spline_coef"). None of the previous help postings seemed to have any solutions to this problem. As per a Ripley suggestion I have deleted all previous versions of R and reinstalled R 2.7.0 and the problem

Hi, I posted some days ago a question concerning the computation of lambda in the smooth.spline function (which I repreat at the bottom of the mail) given _df_ . Unfortunately the documentation is not clear to me. Maybee someone can help to answer in my view the basic question: If the penalized log likelihood is L = (y - f)' W (y - f) + lambda c' Sigma c how the _lambda_ in the above

Full_Name: Tim Heaton Version: 2.8.1 OS: linux-gnu Submission from: (NULL) (143.167.4.162) Hi, In my version of R, the stats package splinefun code for fitting a Fritsch and Carlson monotonic spline does not guarantee a monotonic result. If two adjoining sections both have over/undershoot the way the resulting adjustment of alpha and beta is performed can give modified values which still do

Hi, I am having an issue with a custom panel for lattice. The problem comes when I try passing a groups argument. Here is the custom panel, a wrapper for smooth spline. I copied panel.loess and replaced the loess arguments with smooth.spline(). [Note: I would like to use the cross-validation fitting properties of smooth.spline.] library(lattice)

Suppose I have two var x and y,now I want to fits a natural cubic spline in x to y,at the same time create new var containing the smoothed values of y. How can I get it?