similar to: Weighted Savitzky-Golay?

As the request for the Savitzky-Golay Algorithm in R has come up several times, I here include my implementation based on code written for Matlab. Savitzky-Golay uses the pseudo-inverse pinv() of a matrix. There is an 'generalized inverse' ginv() in the MASS package, but I use a simpler form because I didn't want to 'require' MASS any time I apply Savitzky-Golay.

I got a question from a fellow PhD student that work with spectrum analysis in Excel and now he has lots of spectrums that needs to be smoothed, which would be nice to be able to do in batch. Is there an R package that can do: Savitzky-Golay smoothing for reflectance spectral data or a function that does something similar. _______________________________________ Henrik Andersson

Hi When we use the sav_gol command in R , it shows an error which says: " error in as.matrix". We've downloaded the necessary packages. Kindly help us with this issue. If there is any other function to perform Savitzky-Golay smoothing in R, please let me know. With Regards Reynolds

If I'm not mistaken, that's sort of local polynomial with even degree and fixed bandwidth (based on my own interpretation of description in Numerical Recipes). You can do that with functions in the KernSmooth package. HTH, Andy > -----Original Message----- > From: wolf at micro-biolytics.com [mailto:wolf at micro-biolytics.com] > Sent: Friday, March 21, 2003 1:43 PM > To:

Dear all, Sorry if this is slightly off the track as far as R is concerned, but I have been using the Savitzky-Golay filter to estimate some derivatives of interest. I am wondering however, if anyone has seen anything in the literature (or has any ideas) of how these estimates perform asymptotically. Does anyone know what the rate of convergence is for these? Thanks, matt.

Hi I am running a Savitzky-Golay smoothing filter (http://tolstoy.newcastle.edu.au/R/help/04/02/0385.html) for variables in my dataset, dim (272:90). I managed to run the code for individual variables in the dataset and then combine the results into a single dataset. My novice attempt at this task is shown below csg<-NULL for (i in 1:ncol(data.all)) {

Dear all, Sorry if this is slightly off the track as far as R is concerned, but I have been using the Savitzky-Golay filter to estimate some derivatives of interest. I am wondering however, if anyone has seen anything in the literature (or has any ideas) of how these estimates perform asymptotically. Does anyone know what the rate of convergence is for these as the sample size increases?

Hi the list. I do not understand the philosophy behind numeric and integer. - 1 is numeric (which I find surprising) - 2 is numeric. - 1:2 is integer. Why is that ? Christophe

Hello, I am trying to estimate a multivariate regression of Y on X with regression splines. Y is (nx1), and X is (nxd), with d>1. I assume the data is generated by some unknown regression function f(X), as in Y = f(X) + u, where u is some well-behaved regression error. I want to estimate f(X) via regression splines (tensor product splines). Then, I want to get the predicted values for some new

What is the way to add an arbitrary fit from a model to a lattice conditioning plot ? For example xyplot(v1 ~v2 | v3,data=mydata, panel=function(...){ panel.xyplot(...) panel.loess(...,col.line="red") } ) Will add a loess smoother. Instead, I want to put a fit from lm (but not a simple straight line) and the fit has to be done for each panel

Hi: I am using the package "KernSmooth" to do the local polynomial regression. However, it seems the function "locpoly" can only deal with univariate covaraite. I wonder is there any kernel smoothing package in R can deal with bivariate covariates? I also checked the package "lcofit" in which function "lcofit" can indeed deal with bivariate case. The

Dear R-community, maybe someone can help me with this: I've been using the loess() smoother for quite a while now, and for the matter of documentation I'd like to resolve the acronym LOESS. Unfortunately there's no explanation in the help file, and I didn't get anything convincing from google either. I know that the predecessor LOWESS stands for "Locally Weighted

Hi I have a problem with passing line and symbol parameters to user defined panel functions I had a look at the archives and created a panel function on what was shown and on panel.loess. I could not to get panel.locfit to work for what I intend it for. There is another layer to work with before success as lp() is called from locfit. xx <- structure(list(Farm = c("A",

Hi ilai Thank you for your suggestions. I do not know what happened yesterday I must have omitted a few changes out in going from R to email and apologies for the double posting - I had troubles sending it as my ISP gave a message of not being connected for email but was for the web I was trying to get panel.Locfit to work in a number of situations. 1. Conditioned by Farm (3 panels) with 2

Hello. I have data (plant weights over time) that are non-linear and in which the variance increases over time. I have to estimate the first derivatives of plant weight given time (i.e. growth rate) and their se, using a regression smoother, and I have been considering cubic spline smoothers. However, I do not know if this can be done given that the error variance would increase over time.

Hi, I am using ggplot2 to with the following code: gmathk2 <- qplot(time,math,colour=Kids,data=kids.ach.lm.k5,geom="smooth",method="lm",formula=y~ns(x,1)) + opts(title="Smoother Plot: Math K-5") + xlab("Time") + ylab("Math") + scale_colour_brewer(pal="Set1"); gmathk2 This plots all the smoother for all the x values. What I'd like

Can anyone tell me the trick for obtaining the smoother matrix from smooth.spline when there are non-unique values for x. I have the following code but, of course, it only works when all values of x are unique. ## get the smoother matrix (x having unique values smooth.matrix = function(x, df){ n = length(x); A = matrix(0, n, n); for(i in 1:n){ y = rep(0, n); y[i]=1; yi =

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

On 7 June 2014 00:14, Filip Pizlo <fpizlo at apple.com> wrote: > The only setback is to ensure that we synchronize the renaming with WebKit. > > The WebKit->LLVM interface currently avoids revision-lock; you can take any > recent revision of either and build a working browser engine. This is mostly > true even when we change the stack map format because of versioning in the

Greetings, I would like to use a cubic spline or smoother to model the fixed effects within nlme. So far the only smoother I have been able to get to run successfully in nlme is smooth(). I tried smooth.spline: fixed=list(lKa~1,lCL~smooth.spline(BSA, df=3)) the error I got was the following. Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : invalid