similar to: savitzky-golay derivatives?

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.

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.

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:

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)) {

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

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, Does anyone know how to use weights and generate error bounds for Savitzky-Golay? I have a (smallish) set of points y equally spaced each with a known error and would like to smooth them using S-G but so as to take into account the error already have and construct new error bounds around them that take into account the errors they had at the beginning and the erros they get as a result

Hi, I have estimated a step function and need to take the derivatives of this function at all points in the range. Does anyone know of any clever ways to do this? (I have already tried to fit a polynomial through the points in order to obtain a smooth representation and then take derivatives of this. Also tried to smooth it, and used an SG differentiator. Results are rather poor so far, in

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

Dear list, I hope the topic is of sufficient interest, because it is not R-related. I have N=100 yes/no-responses from a psychophysics paradigm (say Y Yes and 100-Y No-Responses). I want to see whether these yes-no-responses are in line with a model predicting a certain amount p of yes-responses. Standard procedure would be a one-sample binomial test for the observed proportion, chi?(1 df) =

I made one typo in my previous mail.   May I ask one statistics related question please? I have one query on MLE itself. It's property says that, for large sample size it is normally distributed [i.e. asymptotically normal]. On the other hand it is "Consistent" as well. My doubt is, how this two asymptotic properties exist simultaneously? If it is consistent then asymptotically it

Hi, Trying this: deriv(expression(sum(x)), "x") Gives the error message: Function 'sum' is not in the derivatives table I'd like to add it, is this difficult? If not, where is the derivatives table? However, give how basic "sum" is, I suspect it would have been added if it were straightforward. Do functions in the derivatives table need to be

Hi, I'm a recent R convert so I haven't quite figured out the details yet... How do I select one variable by another one? Ie if I want to draw the histogram of variable X only for those individuals that also have a value Y in a certain range? In STATA I would give something like: histogram X if ((Y>=A & Y<=B)) (The data is for individuals and each individual has a number of

Hi, I'm trying to figure out how to have R estimate partial derivatives for logit models. As an example, I'm providing a (fake) scored observation in a MNL with 3 categories of Y and 2 predictors (x01 and x02), and show the right way to calculate it, but am looking for how to use an R function, such as deriv() or anything else. Here is my attempt: ### Variables for an observation x01

Hi. I have been using the splines package for my work, in particular, the bs() function and associated predict() method. I now find myself in need of the derivatives of this beast. In the man page for predict.bSpline I found `predict(object, x, nseg=50, deriv=0, ...)' but alas this is not implemented (deriv= is ignored). I contacted the package maintainers who were most helpful. Bill

Hi R help, I am trying to determine how nls() generates a function based on the self-starting SSlogis and what the formula for the function would be. I've scoured the help site, and other literature to try and figure this out but I still am unsure if I am correct in what I am coming up with. ************************************************************************** dat <-

Dear all, Is there a fairly general R implementation of the delta method that uses numerical derivatives? I realise that the delta method has been implemented using symbolic derivatives (e.g. alr3::delta.method, emdbook::deltamethod, msm::deltamethod and survey:::nlcon), however possibly non-linear estimators using the delta method with numerical derivatives can be quite useful (e.g. predictnl

> Date: Tue, 2 Apr 2013 06:59:13 -0500 > From: Paul Johnson <pauljohn32 at gmail.com> > To: qi A <send2aqi at gmail.com> > Cc: R-help <r-help at r-project.org> > Subject: Re: [R] DUD (Does not Use Derivatives) for nonlinear > regression in R? > Message-ID: > <CAErODj_1pK8raHyAme_2Wt5zQZ_HqOhRjQ62bChhkORWbW=o2A at mail.gmail.com> > Content-Type:

I have been using the rgenoud package for a nonlinear least-squares problem with lots of local minima, and it works very well but takes lots of time. According to the article refrenced in the documentation, the original GENOUD-software by the same authors seems to allow for user-supplied analytical derivatives instead of numerical approximations, which would probably save some time. Does anybody

Hi Konstantinos Not exactly derivative but > diff(df[,2]) [1] -0.01 -0.01 -0.01 -0.01 0.00 0.01 -0.02 -0.03 -0.02 May be enaough for you. Cheers Petr > > -----Original Message----- > From: R-help <r-help-bounces at r-project.org> On Behalf Of konstantinos > christodoulou > Sent: Tuesday, January 31, 2023 10:16 AM > To: r-help mailing list <r-help at