Dear Martin, I have been playing a lot with the glkerns() function in the "lokern" package for "automatic" smoothing of time-series data. This kernel smoothing approach of Gasser and Mueller seems to perform quite well for estimating the function and its derivatives (first and second derivatives). In fact, this is one of the best methods based on my simulation studies for comparing a number of "automatic" bandwidth selection methods. I am interested in applying this to automatic smoothing and feature extraction for a "large" number (thousands) of time series, with hundreds of points per time series. This is where I am interested in seeing if the efficiency of "glkerns" can be improved. Here follows my specific question: You have to call glkerns() separately each time to compute the function and its derivatives, ie. if I want the function and its first 2 derivatives, I have to make 3 calls to glkerns(). This seems to me to be inefficient, especially for large time series. In smooth.spline(), for example, you can call it once to get the fit, and then use the fitted object to compute the derivatives using predict(). Is it possible to have a similar feature in glkerns? Thanks for any suggestions. Ravi. ---------------------------------------------------------------------------- -------
Ravi Varadhan, Ph.D. Assistant Professor, The Center on Aging and Health Division of Geriatric Medicine and Gerontology Johns Hopkins University Ph: (410) 502-2619 Fax: (410) 614-9625 Email: rvarad...@jhmi.edu Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.h tml ---------------------------------------------------------------------------- -------- [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
