similar to: ow to apply a panel function to each of several data series plotted on the same graph in lattice

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)

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) +

I've had a play with this and, due to my own short-comings, remain none the wiser. In particular, I'm not sure what value of 'spar' is consistent with the magic lambda=1/1600 for quarterly data. I initially interpreted spar as lambda and tried setting spar=1/1600. This results in almost no smoothing while spar=1600 causes an error. The smooth.spline function seems to want

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

Hi r-helpers. Please forgive my ignorance, but I would like to plot a smoothing spline (smooth.spline) from package "stats", and show the knots in the plot, and I can't seem to figure out where smooth.spline has located the knots (when I use nknots). Unfortunately, I don't know a lot about splines, but I know that they provide me an easy way to estimate the location of local

Can someone please show me how to smooth time series data that I have in the form of a zoo object? I have a monthly economies series and all I really need is to see a less jagged line when I plot it. If I do something like s <- smooth.spline(d.zoo$Y, spar = 0.2) plot(predict(s,index(d.zoo)), xlab = "Year") # not defined for Date objects and if I do something like

Is there a way of calculating the derivative of a function returned by splinefun()? Such a function is a cubic spline, whence it has a calculable derivative, but is there a (simple) way of getting at it? One workaround that I have thought of is to take a fine grid of points, evaluate the function returned by splinefun() at these points, put an interpolating spline through these points using

Dear all, I'm trying to use the Pspline add-on package to fit a quintic spline (norder =3), but I keep running into a Singularity error. > traj.spl <- smooth.Pspline(time, x, norder=3 ) Error in smooth.Pspline(time, x, norder = 3) : Singularity error in solving equations > Playing around with the other parameters produces an "unused arguments" error: > traj.spl

Dear list, if I do smooth.spline(tmpSec, tmpT, all.knots=T) with the attached data, I get this error-message: Error in smooth.spline(tmpSec, tmpT, all.knots = T) : smoothing parameter value too small If I do smooth.spline(tmpSec[-single arbitrary number], tmpT[-single arbitrary number], all.knots=T) it works! I just don't see it. It works for hundrets other datasets, but not for

Dear R listers, When using smooth.spline to interpolate data, results are generally good. However, some cases produce totally unreasonable results. The data are values of pressure, temperature, and salinity from a probe that is lowered into the ocean, and the objective is to interpolate temperature and salinity to specified pressures. While smooth.spline provides excellent values at the

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 Smoothing Splines, D.G. Pollock (available online) where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) + e_i

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

Everyone, One of our developers was kind enough to insert some bug checking into the mod_auth_pam and mod_auth_sys_group so that we could see a little more of what was going on with our authentication failures. Here is what we just saw. Two of our users NA\connelmp and NA\guminssa both started getting messages that they were not part of the required group. Here is the log for

Please preemptively excuse my ignorance. I'm trying to fit a cubic smoothing spline to a time series according to a method encountered in a paper. The authors state that they fit a spline whose frequency response is 50% at a wavelength of n years where n is 67% of the length of the time series. Is it possible to fit a spline like this in R using the spar parameter in smooth.spline? Or is

Is it really possible that nobody can help me with this? Is r-help too overwhelmed now? Any help appreciated - Jon On 7/16/07, Dr Carbon <drcarbon at gmail.com> wrote: > Please preemptively excuse my ignorance. > > I'm trying to fit a cubic smoothing spline to a time series according to a method encountered in a paper. The authors state that they fit a spline whose frequency

To whom it may concern, I don't know whether this is really a bug with the Pspline package or only a problem with my installation. Things work fine in Linux but not in Mac OS X (Darwin). Both system run the latest public versions of R and Pspline. predict.smooth.Pspline produces only NaN instead of predicted values when norder>2: > library (Pspline) > tt <- seq

Could someone, please, write me, how to compute the spar-value for the smooth.spline-routine to get the same HP-filtered time-series with a parameter lambda for a function (see mail "Hodrick-Prescott-Filter example" from ggrothendieck at yifan.net): hpf <- function(y,lambda{ eye <- diag(length(y)) d <- diff(eye,d=2) z <- solve(eye+lambda*crossprod(d),y)} ? Second, is

Everyone, Here is a challenge for all of you samba experts! Lately I have been seeing a problem where winbind is not correctly identifying a user as a member of a group he most certainly belong to. This is with a Domain Local group so I know samba should support it. Users access a HTTPS (SSL) webpage that is secured by a Domain Local group. Sometimes they get in,

Dear R-helpers: (this is part of a bigger program) the following fails as a function, but runs OK if we comment out the fnfn_ function() line. Any hint would be appreciated. -Yudi- R : Copyright 1998, The R Development Core Team Version 0.63.0 Beta (Nov 13, 1998) -- on WIndows3.11 fnfn _ function (m=10,n=10,spar=2) { fn _ function(u,v){ uc_ u-floor(m/2)-1 vc_ v-floor(n/2)-1

Hi, I am trying to compile R-2.2.1 on Solaris 2.9 with a 64-bit build. Following the instructions in "R Installation and Adminstration", I changed the following settings in "config.site": CC="gcc -m64" F77="g77 -64" CXX="g++ -m64" LDFLAGS="-L/usr/local/lib/sparcv9 -L/usr/local/lib" But I got the following error messages: