similar to: [Mac OS X 10.4] object R_loess_raw not found

I noticed that in https://svn.r-project.org/R/trunk/src/library/stats/R/loess.R that we are now calling R_loess_raw, R_lowesw, R_lowesp, etc. I'm interested to know what is the benefit/reason for the 'R_', as I am unfamiliar with this prefix and do not see it mentioned in 'Writing R Extensions.' Thanks, Ben

Well... this may be a red herring after all, but it's an interesting one! It turns out I have got DYLD_LIBRARY_PATH set. This gets exported to R (or rather, affects the loader that loads the R process?), and somehow mixes up the way symbols are exported. Something that applies to launching R from the command line (/usr/bin/R) AND via LaunchServices (open -a R). Once I noticed that invoking

I noticed a problem using R 2.7.1 on Windows XP SP2 with the precompiled Atlas Rblas.dll. Running the code below causes R to crash. I started R using Rgui --vanilla and am using the precompiled Atlas Rblas.dll from cran.fhcrc.org dated 17-Jul-2007 05:04 for Core2 Duo. The code that causes the crash: x <- rnorm(100) y <- rnorm(100) z <- rnorm(100) loess(z ~ x * y) loess(z ~ x) does

Dear R developers! Considering I got no response or comments in the general r-help forum so far, perhaps my question is actually better suited for this list? I have added some more hopefully relevant technical details to my original post (edited below). Any comments gratefully received! Best regards, David Kreil. ---------- Dear R experts, I have a problem that is a related to the question

Hello, I was hoping that someone familiar with the implementation details of the loess algorithm might be able to help me resolve some difficulties I am having. I am attempting to reproduce some of the functionality of the loess() function in C++. My primary motivation is that I would like to understand the algorithm in detail. So far I have managed to create a working port in C++ for the

Full_Name: Benjamin Tyner Version: 2.1.0, 4/18/2005 OS: i686-redhat-linux-gnu Submission from: (NULL) (4.64.8.220) # Just run my.test() below in a newly opened R session. Once too many models have been fit (~20 on my system), the computed standard error jumps to a different value. This is (superficially) due to a different residual sum of squares, not a different one.delta. No other aspect of

Hi all, I 've got a question about the usage of loess in the modreg package. The documentation (loess.html) states that the smoothing window is either set by span or enp.target. If span is used, the details section of the docs state... <SNIP> DETAILS Fitting is done locally. That is, for the fit at point x, the fit is made using points in a neighbourhood of x, weighted by their

Hi all, I 've got a question about the usage of loess in the modreg package. The documentation (loess.html) states that the smoothing window is either set by span or enp.target. If span is used, the details section of the docs state... <SNIP> DETAILS Fitting is done locally. That is, for the fit at point x, the fit is made using points in a neighbourhood of x, weighted by their

Hello Masters, I run the loess() function to obtain local weighted regressions, given lowess() can't handle NAs, but I don't improve significantly my situation......, actually loess() performance leave me much puzzled.... I attach my easy experiment below #------SCRIPT---------------------------------------------- #I explore the functionalities of lowess() & loess() #because I have

Thanks, John. However, loess.smooth() is producing a very different curve compared to the one that results from applying predict() on a loess(). I am guessing they are using different defaults. Correct? On Thu, 23 Mar 2023 at 20:20, John Fox <jfox at mcmaster.ca> wrote: > Dear Anupam Tyagi, > > You didn't include your data, so it's not possible to see exactly what >

Is there an R implementation of a scheme for automatic smoothing parameter selection with loess, e.g., by minimizing one of the AIC/GCV statistics discussed by Hurvich, Simonoff & Tsai (1998)? Below is a function that calculates the relevant values of AICC, AICC1 and GCV--- I think, because I to guess from the names of the components returned in a loess object. I guess I could use

Dear Anupam Tyagi, You didn't include your data, so it's not possible to see exactly what happened, but I think that you misunderstand the object that loess() returns. It returns a "loess" object with several components, including the original data in x and y. So if pass the object to lines(), you'll simply connect the points, and if x isn't sorted, the points

I have a question about running an optimization function on an existing LOESS function defined in R. I have a very large dataset (1 million observations) and have run a LOESS regression. Now, I want to run a Newton-Raphson optimization to determine the point at which the slope change is the greatest. I am relatively new to R and have tried several permutations of the maxNR and nlm functions with

Dear R community, I am trying to understand how the predict function, specifically, the predict.loess function works. I understand that the loess function calculates regression parameters at each data point in 'data'. lo <- loess ( y~x, data) p <- predict (lo, newdata) I understand that the predict function predicts values for 'newdata' according to the loess regression

Hi! In a current project, I am fitting loess models to subsets of data in order to use the loess predicitons for normalization (similar to what is done in many microarray analyses). While working on this I ran into a problem when I tried to predict from the loess models and the data contained NAs or NaNs. I tracked down the problem to the fact that predict.loess will not return a value at all

I'm updating the loess routines to allow for, among other things, arbitrary local polynomial degree and number of predictors. For now, I've given the updated package its own namespace. The trouble is, panel.loess still calls the original code in package:stats instead of the new loess package, regardless of whether package:loess or package:lattice comes first in the search list. If I

Hi Folks, I'm looking to do Confidence bands around LOESS smoothing curve. If found the older post about using the Standard error to approximate it https://stat.ethz.ch/pipermail/r-help/2008-August/170011.html Also found this one http://www.r-bloggers.com/sab-r-metrics-basics-of-loess-regression/ But they both seem to be approximations of confidence intervals and I was wonder if there was

Not sure what I'm doing wrong. Can't seem to get loess values. It looks like loess is returning the same values as the input. j <-loess(x1$total~as.numeric(index(x1) plot(x1$total,type='l', ylab='M coms/y global',xlab='') lines(loess(total~as.numeric(index(x1)),x1)) The plot statement works fine No errors with the "lines" statement But I don't

Hi everyone. I'm trying to break the y axis on a plot. For instance, I have 2 series (points and a loess). Since the loess is a "continuous" set of points, it passes in the break section. However, with gap.plot I cant plot the loess because of this (I got the message "some values of y will not be displayed"). Here's my code: library(plotrix); #generate some data x

Hi, I posted a message earlier entitled "How to fit a line through the "Mountain crest" ..." I figured loess is probably the best way, but it seems that the problem is the robustness of the fit. Below I paste an example to illustrate the problem: tmp=rnorm(2000) X.background = 5+tmp; Y.background = 5+ (10*tmp+rnorm(2000)) X.specific = 3.5+3*runif(1000);