similar to: How to fit a line through the "Mountain crest", i.e., through the highest density of points - in a "loess-like" fashion.

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);

Hello, 1) I'm planning on adding lots of measurements to the driver liebert-esp2 + correct support for 3-phase systems. Should I make a large patch based on the last svn head or send you the whole file? Multiple patches per change are probably not practical in this case. 2) in http://www.networkupstools.org/doc/2.2.0/new-names.html there is no crest factor variable. Crest factor is very

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

Gurus, I used predict(loess(Y~X)); where Y and X are of the same length. But there are same NA's in both Y and X. Those NA's are in the same locations in Y and X. The following is the error messageI got: Error in "[<-"(*tmp*, , i, value = predict(loess(Y~X))) : number of items to replace is not a multiple of replacement length If I replaced the NA's with a

Hello List, I have a longitudinal samples on multiple individuals, for initial analysis, i am doing some plots to see how the variable changes with the age. In some of the individuals one or two data points are missing, IL1Ra Age.at.Sample.Collection Subject.ID 6.575466 2.004106 00709-0 NA 2.162902

I'm at my wit's end, and have searched all of my sources. I need to generate a relatively large number of individual LOESS fits each month of data (I have about 16 months of data). Fitting the polynomial is not my problem, figuring out what the formula that describes that polynomial is. apologies in advance if this is so simple, but I need a hand here. Thanks. -- View this message in

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

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

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

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

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

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 am trying to carry out density estimation for three dimensions (with anywhere between a few hundred and ~8000 data points). Which leads me to ask: a) is there any equivalent to kde2d (in MASS) or bkde2D (in KernSmooth) out there for three dimensions? b) if not, my skills only seem to extend as far as writing a function which measures density as the number of data points falling within a

Hi, I am trying to create a loess smooth from hydrologic data. My goal is to create a smooth line that describes discharge at a certain point in time. I have done this using the 'lowess' function and had no problem, but I'm having some difficulty with loess. I am inputting the date ('date') and discharge ('q') values using the 'scan' function, then inputting

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

Hi, ? I am trying to write a shapefile with projection. I have my data in a data.frame called try and consists in xy coordinates and a numerical attribute value z1. ? Libraries loaded are: sp, rgdal, raster, maptools ? head(try) ???????? x?????? ??? y??? ? ? ? ? ? ? ??? z1 1 610237.1 ???????? 3375751 ???????????? 8.221 2 610236.1 ???????? 3375750 ???????????? 8.153 3 610236.1 ????????