similar to: Is there an implementation loess with more than 4 parametric predictors or a trick to similar effect?

Dear R experts, I have a problem that is a related to the question raised in this earlier post ??? https://stat.ethz.ch/pipermail/r-help/2007-January/124064.html My situation is different in that I have only 2 predictors (coordinates x,y) for local regression but a number of global ("parametric") offsets that I need to consider. Essentially, I have a spatial distortion overlaid over a

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

Dear John, > I suggest that you look at the abilities of the mgcv package. > There are notes of mine at > > http://www.maths.anu.edu.au/%7Ejohnm/r-book/xtras/autosmooth.pdf > > that may help you get started. Thank?you very much for the suggestion and the link to your write-up, it was indeed very helpful! I have experimented with this library for a while now and am really happy

Hello, I found a little BUG in loess <stats>. It does not receive the iterations parameter. It can be debugged in the following way: THIS IS AN EXCERPT FROM THE CODE: .... fit <- simpleLoess(y, x, w, span, degree, parametric, drop.square, normalize, control$statistics, control$surface, control$cell, iterations, control$trace.hat) Replace argument iterations with

Hi, I have a data frame with 6563 observations. I can run a regression with loess using four explanatory variables. If I add a fifth, R crashes. There are no missings in the data, and if I run a regression with any four of the five explanatory variables, it works. Its only when I go from four to five that it crashes. This leads me to believe that it is not an obvious problem with the data,

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, I am segfaulting when using predict.loess() (checked with r62092). I've traced the source with the help of valgrind (output pasted below) and it appears that this is due to int overflow when allocating an int work array in loess_workspace(): liv = 50 + ((int)pow((double)2, (double)D) + 4) * nvmax + 2 * N; where liv is an (global) int. For D=1 (one x variable), this overflows at

Hello, Does anyone know of an R version of loess that allows more than 4 predictors and/or allows the specification of offsets? For that matter, does anyone know of _any_ version of loess that does either of the things I mention? Thanks, Paul Louisell 650-833-6254 ploua@allstate.com Research Associate (Statistician) Modeling & Data Analytics ARPC [[alternative HTML version deleted]]

Greetings and happy new year! I am in the process of converting some of the old S-PLUS scripts from Visualizing Data (Cleveland, 1993) into lattice. In fact, I did most of it several years ago, and at the time, all of the scripts that contained loess() worked fine. Tonight, I ran most of the scripts again, but every one that I tried with a loess() call crashed R. I tried it in two sessions, one

I am exploring the use of loess for oceanographic applications and would like to plot the locations (longitude and latitude) points where the models (salinity~temperature*longitude*latitude,parametric="temperature") are fitted. Chambers and Hastie(1993) explains the locations are nodes of a k-d tree. but I cannot find anything about accessing this information. It would be useful to

Hi there, I would like to draw a scatter plot and fit a smooth line by loess. Below is the data. However, the curve line started from 0, which my "resid" list doesn't consist of 0 value. It returned some warnings which I don't know if this is the reason affecting such problem. Here I also attached the warning messages. Please let me know if there is a solution to fix this. Thank

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

hi! i want to change the "robustness weights" used by loess. these are described on page 316 of chambers and hastie's "statistical models in S" book as r_i = B(e_i,6m) where B is tukey's biweight function, e_i are the residulas, and m is the median average distance from 0 of the residuals. i want to change 6m to, say, 3m. is there a way to do this? i cant

Please do as we ask (repeatedly) and study the help page before posting. 'family' is a separate argument, not part of loess.control, as the help page correctly documents. If you use cars.lo2 <- loess(dist ~ speed, cars, family = "symmetric", control = loess.control(surface = "direct", iterations = 20)) cars.lo2$pars$iterations it prints *20*, as it is

Hello, I am wondering if there is an easy way to combine loess() with glm() to produce a locally fitted generalised regression. I have a data set of about 5,000 observations and 5 explanatory variables, with a binary outcome. One of the explanatory variables (lets call it X) is much more predictive than the others. A single glm() regression over the entire data set produces rather poor results,

Hi, I'm wondering which function would allow fitting this type of data: tmp=rnorm(2000) X.1 = 5+tmp Y.1 = 5+ (5*tmp+rnorm(2000)) tmp=rnorm(100) X.2 = 9+tmp Y.2 = 40+ (1.5*tmp+rnorm(100)) X.3 = 7+ 0.5*runif(500) Y.3 = 15+20*runif(500) X = c(X.1,X.2,X.3) Y = c(Y.1,Y.2,Y.3) plot(X,Y) The problem with loess is that distances for the "goodness of

hi all i have a another stats question. i would like to solve the following question: y(i)=a+b*x(i)+e(i) i.e. estimate a and b (they should be fixed) but i dont want to specify the standard density to the straight line. this can be done using kernel regression. the fitted line is however fitted locally. does anyone have a reference that will help me with my problem. i am still new to

Hi, I am using Loess.smooth (Modreg) in order to infer certain relationship for the data set of ~130,000 observations with ~300 distinct values of single predictor. I understand that fitted values (y-hat) are just 300 Weighted LS fits in certain neighborhood of predictors. I am bit confused about how exactly is this neighborhood assigned . Say I choose spanning parameter = .5, for each LS

Hello, I am interested in performing a 2D loess smooth on microarray data, i.e. log2 ratios on a 2D grid, using different spans in the horizontal and vertical directions (the immediate reason being that replicate spots are laid out in the horizontal direction). Is it possible to do this in R? Functions like loess(stats) seem to apply the same span for all predictors, which carries over to