similar to: smoothing noisy data with a twist

Good evening,I am Marta Colombo, student of "Politecnico di Milano". I'm studying Local Regression Techniques such as loess, smoothing splines and kernel smoothers. Choosing "symmetric" for the argument "family" in loess function it is possible to produce a robust estimate , in function smooth.spline and ksmooth I didn't find this possibility. Well, is there a

Hi there, I'm using the lattice package to create an xy plot of abundance vs. depth for 5 stages of barnacle larvae from 5 species. Each panel of the plot represents a different stage, while different loess smoothers within each panel should represent different species. However, I would like depth to be on the y-axis and abundance to be on the x-axis, because this is more intuitive as an

loess() should be able to do robust 2D smoothing. There's no natural ordering in 2D, so defining running medians can be tricky. I seem to recall Prof. Koenker talked about some robust 2D smoothing method at useR! 2004, but can't remember if it's available in some packages. Andy From: Vladislav Petyuk > > Hi, > Are there any multidimenstional versions of runmed() and >

Hi ilai Thank you for your suggestions. I do not know what happened yesterday I must have omitted a few changes out in going from R to email and apologies for the double posting - I had troubles sending it as my ISP gave a message of not being connected for email but was for the web I was trying to get panel.Locfit to work in a number of situations. 1. Conditioned by Farm (3 panels) with 2

Hi R Users, I am very new to R and would like to do something quick if possible, please help! Suppose I have a data set of y versus x, how can I generate a smoothing line of y versus x (for example, using loess) and at the same time, generate a pair of confidence intervals for the smoothing or mean plus/minus standard deviation? Yi Zhu Golder Associates Inc. USA

Hi I have a problem with passing line and symbol parameters to user defined panel functions I had a look at the archives and created a panel function on what was shown and on panel.loess. I could not to get panel.locfit to work for what I intend it for. There is another layer to work with before success as lp() is called from locfit. xx <- structure(list(Farm = c("A",

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, In reading the loess description I see: span: the parameter alpha which controls the degree of smoothing. The default seems to be 0.75. Would it be possible to expand on this decription so I can avoid trail and error? Can I increase this pass 'span' > 1? Qualitatively to what degree changing this value affects the smoothing of the data? Thank you. Kevin

Hi all, I'm having two small problems and was wondering if you could help me out. Problem 1: When I use pch to place a symbol onto a plot it seems to place the point a little off the exact location. I have read the help file for this which says to use "." as the plotting character, which will then produce centered points but I am unclear as to how to do this. Problem 2: How do I

I have contributed a "gam" library to CRAN, which implements "Generalized Additive Models". This implementation follows closely the description in the GAM chapter 7 of the "white" book "Statistical Models in S" (Chambers & Hastie (eds), 1992, Wadsworth), as well as the philosophy in "Generalized Additive Models" (Hastie & Tibshirani 1990,

I have contributed a "gam" library to CRAN, which implements "Generalized Additive Models". This implementation follows closely the description in the GAM chapter 7 of the "white" book "Statistical Models in S" (Chambers & Hastie (eds), 1992, Wadsworth), as well as the philosophy in "Generalized Additive Models" (Hastie & Tibshirani 1990,

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 have a set of data (in a matrix). I spliced a column out and parsed it as.ts (time series). I then plotted the time series but I found that it was very noisy. I wanted to smooth it out. However, I am having some problems smoothing and plotting the smoothed version. > A <- as.ts(read.table(choose.files())) > x <- as.ts(A[,10]) > plot(x) > > plot(smooth(x))

R Friends, I have data from which I would like to learn a more general (smoothened) trend by applying data smoothing methods. Data points follow a positive stepwise function. | x x | xxxxxxxx xxxxxxxx | x x |xxxx xxx xxxx | xxxxxxxxxxxxxxxxx | |

Hi, I'm unable to find a way to smooth data for a persp() graph. Example, suppose that I have data x,y,z like this: x <- 1:10 y <- 1:10 k <- 20 z <- outer(x, y, "*") + matrix( k*runif(100, -1, 1), 10, 10) persp(x, y, z, theta = 35, phi = 25) The graph is not very nice. Is there a way to smooth the z data so that at the end the graph would look more like something

I have a problem either understanding what loess is doing or that loess has a problem itself. As the x-axis variables become more concentrated on a particular point,the estimated loess tends to zero????. the examples below show what i am talking about, why is that? my intution tells me that it should tend to the mean of the variable which is been smoothed. Here's a worked up example x

Hello, I am fairly new to R am stumped on how to get the xyplot function in the lattice package to produce a scatter plot of count data versus time, such that the count data represent 8 different groups, and the plot produced has 8 unique "smoothed" lines for the different groups. This is the closest I can get to the desired plot: xyplot(masmean ~ mas, data = Pre96CR.masmeans, groups

Hi there, I cant seem to find a way for the lowess smoothing function to handle "NA" values. Can anyone help?? Regards, Wayne Dr Wayne R. Jones Statistician / Research Analyst KSS Group plc St James''s Buildings 79 Oxford Street Manchester M1 6SS Tel: +44(0)161 609 4084 Mob: +44(0)7810 523 713 KSS Ltd A division of Knowledge Support Systems Group plc Seventh Floor St

Hi: I am using the package "KernSmooth" to do the local polynomial regression. However, it seems the function "locpoly" can only deal with univariate covaraite. I wonder is there any kernel smoothing package in R can deal with bivariate covariates? I also checked the package "lcofit" in which function "lcofit" can indeed deal with bivariate case. The