similar to: Loess and predict

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

Hi, For some reason I have been unable to use the predict function when I desire the standard error to be calculated too. For example, when I try the following: l<- loess(d~x+y, span=span, se=TRUE) p<- predict(l, se=TRUE) I get the following error message: Error in vector("double", length) : vector size cannot be NA In addition: Warning message: In N * M1 : NAs produced by

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

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 <-

Hello, I have the following data1 (index are chars): ?? ?index 1 ?008823 2 ?012689 3 ?004503 4 ?002991 5 ?012689 6 ?002845 7 ?012689 8 ?012395 9 ?012689 10 009302 11 002845 12 006669 13 008823 14 009302 15 025340 16 012689 and data2 in this format (index2 are chars): ?? ? ? index2 ? ? ?tic 1 ? ? 001003 ? ? ANTQ 2 ? ? 001004 ? ? AIR 3 ? ? 001009 ? ? ABSI 4 ? ? 001011 ? ? ACSE etc I am

Hello, I need help with the details of loess prediction algorithm. I would like to get it implemented as a part of a measurement system programmed in LabView. My job is provide a detailed description of the algorithm. This is a simple one-dimensional problem - smoothing an (x, y) data set. I found quite a detailed description of the fitting procedure in the "white book". It is also

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

Dear altogether, I tried local regression with the following data. These data are a part of a bigger dataset for which loess is no problem. However, the plot shows extreme values and by looking into the fits, it reveals very extreme values (up to 20000 !) although the original data are > summary(cbind(x,y)) x y Min. :1.800 Min. :2.000 1st Qu.:2.550

Hello, I'm attempting to evaluate the accuracy of the probability predictions for my model. As previously discussed here, the AUC is not a good measure as I'm not concerned with classification accuracy but probability accurcy. It was suggested to me that the loess function would be a good measure to look at. I can see some libraries (Design) will plot the loess function as a curve

Hello, I'm attempting to evaluate the accuracy of the probability predictions for my model. As previously discussed here, the AUC is not a good measure as I'm not concerned with classification accuracy but probability accurcy. It was suggested to me that the loess function would be a good measure to look at. I can see some libraries (Design) will plot the loess function as a curve

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

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 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

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

Hi Every Body, I don't know why some regression functions have no related prediction function. For example lowess, loess, lokerns, lpridge, lpepa, and ksmooth. What could help? Is there any global or wrapper function so that can help? Regards, Amir Safari --------------------------------- [[alternative HTML version deleted]]

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 >

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

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

Hi, I have 2 questions regarding using loess function in stats package. 1. I have about 1 million points. I did loess with alpha=0.5 and degree=1 and 2 regressors. It has run for more than 5 hrs on 64bit 16CPU and 64GB server (with no other process running). I am wondering if this is usual? And if there is anyway to make it run faster? 2. I noticed a difference in RAM consumption when specifying