similar to: tri-cube and gaussian weights in loess

Displaying 20 results from an estimated 6000 matches similar to: "tri-cube and gaussian weights in loess"

2003 May 18
2
derivatives from loess (not locpoly)?
is there a way of estimating derivative curves, similar to the ones we get from 'locpoly', from 'loess' estimation. i am interested in estimation of 1st and 2nd derivatives... --------------------------------- [[alternate HTML version deleted]]
2012 Mar 06
1
LOESS confidence interval
Dear all, I'm trying to construct confidence intervals for a LOWESS estimation (by not using bootstrapping). I have checked previous posts and other material online and I understand that the main procedure is: my.count<- seq(...) fit<- loess (y ~ x, data=z) pred<- pred(fit, my.count, se=TRUE) and then the plotting. However, it's not working; as confidence
2005 Nov 17
3
loess: choose span to minimize AIC?
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
2008 Oct 10
3
predicting from a local regression and plotting in lattice
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 <-
2002 Oct 31
3
Loess with glm ?
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,
2014 Oct 07
3
lattice add a fit
What is the way to add an arbitrary fit from a model to a lattice conditioning plot ? For example xyplot(v1 ~v2 | v3,data=mydata, panel=function(...){ panel.xyplot(...) panel.loess(...,col.line="red") } ) Will add a loess smoother. Instead, I want to put a fit from lm (but not a simple straight line) and the fit has to be done for each panel
2010 Feb 22
4
Alternatives to linear regression with multiple variables
I wonder if someone can give some pointers on alternatives to linear regression (e.g. Loess) when dealing with multiple variables. Taking any simple table with three variables, you can very easily get the intercept and coefficients with: summary(lm(read_table)) For obvious reasons, the coefficients in a multiple regression are quite different from what you get if you calculate regressions for
2002 Sep 15
7
loess crash
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,
2013 Jan 08
1
Problem getting loess tricubic weights
Hi I am trying to get the tricube weights from the loess outputs as I need to calculate an error function which requires the weight. So I have used the following example from the R: cars.lo <- loess(dist ~ speed, cars, span=0.5, degree=1, family="symmetric") Then i try to get the weights: cars.lo$weights [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
2007 Jun 26
3
[PATCH] Always use mipmaps in cube plugin
Currently, the cube plugin uses mipmapping only when cube is unfolded. When the cube is rotated, mipmaps are not used, which leads to ugly look of textures. The attached patch fixes this. This leads to another kind of artifacts, which are fixable with anisotropic filtering. http://team.pld-linux.org/~wolf/aniso.png The leftmost image is the current state of cube plugin. The center image is with
1998 Mar 17
0
R-beta: locfit -> CRAN
The locfit library is now available through CRAN, in the Contributed R Code directory. Locfit fits local regression, likelihood and density estimation models, in the spirit of loess but with many additional features. To install, unpack the locfit_19980309.tar.gz file, and R INSTALL locfit Most of the functionality and examples on my home page http://cm.bell-labs.com/stat/project/locfit/ should
1998 Mar 17
0
R-beta: locfit -> CRAN
The locfit library is now available through CRAN, in the Contributed R Code directory. Locfit fits local regression, likelihood and density estimation models, in the spirit of loess but with many additional features. To install, unpack the locfit_19980309.tar.gz file, and R INSTALL locfit Most of the functionality and examples on my home page http://cm.bell-labs.com/stat/project/locfit/ should
2007 Apr 12
1
[PATCH] Transparent cube
Hi, Recently i have worked on re-writing beryl's transparent cube, and ported 3d plugin to compiz. I'm attaching a patchset here that includes the transparent cube patches (i'll post the 3d plugin when i fix some problems that didn't happen in beryl). Patching order: btf-ftb.patch, clip-planes.patch, plugin-events.patch, cube-paint-order.patch, transparent-cube.patch. Special
2006 Dec 05
2
workspaces/desktops/viewports on top/bottom of cube?
I know this has probably been asked before, but, I'll ask anyways: Workspaces/Desktops/whatever on the top and bottom of the cube, is there any chance this will ever happen? I love how in gnome you can have work spaces in rows/columns, and i tend to have 4-6 workspaces (well 6, but only 4 gets used all of the time, but its nice to have the "buffer" workspaces. since using compiz
2005 Jul 12
1
getting panel.loess to use updated version of loess.smooth
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
2023 Mar 23
1
loess plotting problem
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 >
2011 Jul 12
1
LOESS function Newton optimization
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
2023 Mar 23
1
loess plotting problem
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
2012 Apr 03
2
How does predict.loess work?
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
2010 Oct 26
2
anomalies with the loess() function
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