similar to: Alternatives to linear regression with multiple variables

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,

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

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 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 <-

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",

>From what I understand, loess in R uses the standard tri-cube function. SAS/INSIGHT offers loess with Gaussian weights. Is there a function in R that does the same? Also, can anyone offer any references comparing properties between tri-cube and Gaussian weights in LOESS? Thanks. - Andr? -- View this message in context:

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]]

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

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

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,

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

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

Dear all, please do you have any recommendation about a more advanced function in R for lowess/loess regression ? the basics lowess() or loess() do not perform as well as I would expect. thanks very much, Bogdan [[alternative HTML version deleted]]

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, I'm trying to fit nonlinear mixed effects model using nlme function but getting an error message. Here is what I have: fitted_model = nlme(scores~spline(b1,b2,b3,kt,time), fixed = list(b1~1, b2~1, b3~1, kt~1), random = b1+b2+b3~1, groups= ~id, data = sdat, start = c(b1=3.5,b2=2,b3=.60,kt=3.5),verbose=T) Error: Error in

Hi All, In another thread Andy Liaw, who CRAN lists as locfit maintainer; said: <quote> From: "Liaw, Andy" <andy_liaw at merck.com> To: "Guy Green" <guygreen at netvigator.com>; <r-help at r-project.org> Subject: Re: Alternatives to linear regression with multiple variables Date: 22 February 2010 17:50 You can try the locfit package, which I believe

Greetings and happy new year! I am in the process of converting some of the old S-PLUS scripts from Visualizing Data (Cleveland, 1993) into lattice. In fact, I did most of it several years ago, and at the time, all of the scripts that contained loess() worked fine. Tonight, I ran most of the scripts again, but every one that I tried with a loess() call crashed R. I tried it in two sessions, one

I'm having difficulties with plot.locfit.3d, at least I think that is the problem. I have a large dataframe (about 4 MM cases) and was hoping to see a non-parametric estimate of the hazard plotted against two variables: > fit <- locfit(~surv.yr+ ur_protein + ur_creatinine, data=TRdta, cens = 1-death, family = "hazard", xlim=c(0,10)) # it took somewhere between 1 and 2

Hi, I am using locfit for regression. After I do out = locfit(...), I want to save "out" in a file, and load it later for prediction. How should I do it? Thanks!

Hello! In the book Modern Applied Statistics with S (4th ed), section 5.6 the concept of the "average shifted histogram" or ASH is mentionend. Also it is mentioned in the same section "The code used is in the scripts for this chapter" (from figure caption 5.8, analysis of the geyser duration data). *However*, I have trouble finding the code for that function! Admittedly, I am a