similar to: Spline - frequency response (again)

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

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

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

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

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

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

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 is it possible to fit a trend line (or some other panel function) through each of multiple data series plotted on the same graph? Specifically, while one can do something like xyplot(a+b+c~x) which plots three series, a,b & c, but can one automatically fit lines through each of them? I suppose one could generate three more variables afit, bfit, and cfit with a model & predict and

Hola compañeros, os necesito! Necesito la ayuda de personas con experiencia en splines. Estoy filtrando el vector 'v' (velocidad) así: v <- smooth.spline( 1:length(v), v, spar=0.7) Me gustan los resultados pero el problema es que cambian en función de la longitud del vector. Esta imagen corresponde al movimiento vertical de una barra de pesas (para los curiosos, el ejercicio el el

Hello, I am using R and the smooth.Pspline function in the pspline package to smooth some data by using natural cubic splines. After fitting a sufficiently smooth spline using the following call: (ps=smooth.Pspline(x,y,norder=2,spar=0.8,method=1) [the values of x are age in years from 1 to 100] I tried to check that R in fact had fitted a natural cubic spline by checking that the resulting

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

Hello R-Users, I have recently run into several problems using vgam() in the VGAM package. I am hoping someone might have some solutions... Briefly, I have been trying to fit GAM models for zero-altered negative binomial models. 1. When fitting smoothed parameters (e.g. s(X, df=2)) changing the degrees-of-freedom has no effect on the level of smoothing (e.g. number of knots for the

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)

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

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

Hi I'm trying to write a generic script for processing some data which finishes off with some plots. Given Im never sure how many columns will be in my dataframe I wanted to using the following plot(spectra.wavelength, cormat, type = "l", ylim=c(-1,1), xlab="Wavelength (nm)", ylab="Correlation") however even if I specify as type="l" it appears plot

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

I've rolled up R-1.2.3.tgz a moment ago. This is mainly adds fixes to the Macintosh port, but it also has a number of bugfixes for all versions. You can get it from the developer site ftp://cvs.r-project.org/pub/CRAN/src/base/R-1.2.3.tgz or http://cvs.r-project.org/pub/CRAN/src/base/R-1.2.3.tgz or wait for it to be mirrored at a CRAN site near you within a day or two. Binaries for

I've rolled up R-1.2.3.tgz a moment ago. This is mainly adds fixes to the Macintosh port, but it also has a number of bugfixes for all versions. You can get it from the developer site ftp://cvs.r-project.org/pub/CRAN/src/base/R-1.2.3.tgz or http://cvs.r-project.org/pub/CRAN/src/base/R-1.2.3.tgz or wait for it to be mirrored at a CRAN site near you within a day or two. Binaries for