similar to: Exporting the formula for a LOESS fit

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

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

I have a two-dimensional loess fit, and need to calculate the gradient field from it. Even after looking at loess.c and loess.f, I don't understand the meaning of the returned polynomial coefficients. Or is the brute force method of using a tangential approx to the fitted values the way to go? Dieter Menne --------------------------------------- Dr. Dieter Menne Biomed Software 72074

Background: I'm a student of Prof. Cleveland at Purdue University. Eventually, we'd like to release a new version of the loess routine in R. For starters, this implementation would have support for local polynomial degree 3, better control over the number of cells in the KD tree, and perhaps a better solution in higher predictor dimension. I see that Prof. Ripley was responsible for

Hi, I wonder why my attempt to extend an existing loess fit to a new data set is producing error. I was trying the following: dat = read.csv(choose.files()) x = dat[,2]; y = dat[,1] x.sort = sort(x) y.loess = loess(y~x, span=0.75) # For testing the above fit with a new dataset: test = read.csv(choose.files()) # test data new_x = test [,1]; new_y = test[,2] new_x.sort = sort(new_x) predicted

Hi, I'm trying to normalize data by fitting a line through the highest density of points (in a 2D plot). In other words, if you visualize the data as a density plot, the fit I'm trying to achieve is the line that goes through the "crest" of the mountain. This is similar yet different to what LOESS does. I've been using loess before, but it does not exactly that as it takes

R folks, I am somewhat new to R and have started to stumble... I have a set of data that I am trying to model, so that I can predict on a much larger set - and I have been using loess to get a model.. but it is not what I would like to see. For instance, I know from the nature of the data that the shape of this line should only decrease, and yet the loess is being affected by the sample

Hello: I recall something like this being discuss recently, but I can't seem to locate an example in the archives. I have data like the following: df <- expand.grid(1:4, 1992:2002) names(df) <- c("MSA", "YEAR") df$IDUPREV <- runif(44) df$VALIDAT <- rnorm(44) I want to create an xyplot() with separate loess lines for each series (IDUPREV and VALIDAT) in

These data represent stream channel cross-sectional surveys. I would like to be able to find the measurement on the tape (measure) where the Bank Full Depth (bkf_depths) is 0. This will happen twice because the channel has two sides. I thought fitting a loess line to these data and then predicting the measurment number would do it. I was wrong. Below is my failed attempt. My naive thought is

Hello R users & R friends, I just want to ask you if density() can produce a confidence interval, indicating how "certain" the density() line follows the true frequency distribution based on the sample you feed into density(). I've heard of loess.predict(loess(y ~ x), se=TRUE) which gives you a SE estimate of the smoothed scatterplot - but density() kernel smoothing is not the

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 R community, I'm running R 2.7.2 on Windows XP SP2. I'm trying to (1) plot loess lines for each of my groupings using the same color for each group; (2) plot loess predicted values. The first part is easy: data1 <- data.frame(Names=c(rep("Jon",9),rep("Karl",9)),Measurements=c(2,4,16,25,36,49,64,81,100,1,2,5,12,17,21,45,54,67),PlotAt=c(1:9,1:9)) data2 <-

Good morning, I am Marta Colombo,student at Politecnico,Milan. I am studying local regression models and I am using loess function. My problem is that when I have a loess object I don't know how to display the fitted surface; in fact, while in S when you have a loess object you can see it writing plot(object), in R this dosen't work. Also I'd like to know if there is something like the

Dear R-Listers, I have spatial data on an equidistant rectangular grid, similar to topographic data. I know that there are quite a few R-packages or base functions that provide nice iso-contours plot, but I don't want a plot, just the smoothed isocontour line of ONE level (e.g. 10 mm). Data sets are large, so it would be preferable if the availability of regular grid data could be exploited,

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 >

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 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

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

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