similar to: locfit and memory allocation

Full_Name: Somkiat Apipattanavis Version: 2.1.1 OS: Windows Submission from: (NULL) (128.138.44.123) Bug found in predict.locfit for density estimation # Example of bug found in prdict.locfit (Locfit) library('locfit') # generate data y =c(4281,2497,4346,5588,5593,3474,4291,2542,5195,4056, 3114,2864,4904,7625,3377,4001,4999,7191,8062,5668) x1=c( 0.258729, 1.460156, 0.192323,

Apologies for the coming to this late... 1. By now I hope Somkiat has realized that R-bugs is not the place to report problems in contributed packages. Please direct such reports to the package maintainer. 2. This is really user error. predict() expect the newdata to be a data frame containing variables with the same names as those used in the fitting process. E.g., you fitted the model with

I was wondering if someone could help me with a problem getting locfit running. I am running Digital Unix 3.2D on an alphastation, with R 0.61.1 I downloaded the CRAN distribution of locfit. I had no problems installing it with R INSTALL (I installed it into my library rather than the default base library; the output is attached at the end of this email message). I then tried to test it with the

Hi, I?m trying to combine the Locfit Package with the Mgcv package (to use Generalized Additive Models with Locfit components). I read the book written by Clive Loader where it?s said that, for the S language, you just have to "load" the locfit package using the command : Library(locfit, first="T") in order to use locfit components in an additive model. But I can?t. I guess

Hello there, I am presently using the locfit package in "R" and would appreciate some help here. Could anyone tell me tell me how I can obtain the various components (x and y values)of the density estimation after using the "R" command "locfit"? For example, with the command "fhat<-density(somename)" I can obtain the x and y values by simply calling

Hi list, This was asked a couple of years ago but I can't find a resolution. Is there any way to get the coefficients from one of the local polynomial fits in locfit. I realize that locfit only constructs polynomials at a handful of intelligently selected points and uses interpolation to predict any other points. I would like to know the terms of the polynomials at these points. It seems

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

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

Dear, I am doing some statistics analysis using R software. I am currently trying to implement your library locfit. I am using Win NT and R1.1.1 version obtained on the following Webmaster : http://cm.bell-labs.com/cm/ms/departments/sia/project/locfit/index.html When trying to run it, the following message appears: ********** Error in .C("guessnv", nvm = integer(1), ncm =

The version of locfit on the web site mentioned apparently has been revised by Prof. Loader, and is newer than the CRAN version that I have been maintaining. If Prof. Loader is OK with it, I will take a look and see if I can get the new version into CRAN-conforming form and upload to CRAN. Meanwhile, make sure you're using the package from Prof. Loader's web page, instead of the one on

Hello!!! I'm just a beginner of R. I work with locfit to get the density from an numeric vector. It looks good when I plot the graph, but I need the output data of locfit. For example I need somthing like that (only integer values for the input): numeric vector (input of locfit) density (output of locfit) 1 0.05.... 2 0.04.. 3 0.01 ... Thank you for your help and your time.

Dear all, I am trying to include confidence intervals in a xyplot. This is what I am doing: xyplot(x ~ y|z, alpha = 1,band = "global",panel = panel.locfit) (more specifically, in my case x is a binary response from a logistic regression model) The output plot was fine but it did not include the confidence intervals Anyone knows how to do it? (xYplot did not work either) many thanks

I have a problem with the use of locfit with censured data, when I carry out locfit by: fitbmt<-locfit(~recur,data=BMTAGE11,cens=df.status,family="hazard",alpha=0.5) it does not give me any message, but if I want to obtain the graph or even if I ask for (fitbmt) made it gives me the following message: > fitbmt31 Problem: Object "fitbmt31" not found, while calling

Hello!!! Thank you all for your suggestion on my last question about locfit. Now I have another question: I would like to change the evalution structure ev in locfit() to a vector/matrix. I created a vector and a matrix: > vec <- mat.or.vec(101, 1) > vec <- c(0:100) > mat <- matrix(data = vec, nrow = 101, ncol = 1) and I tried to change the ev parameter: > fit <-

I have a dataset of pregnancy values for multiple years (and ages, not included) with missing years. I would like to use local polynomial regression to smooth the values and estimate for the missing years. I would also like to use GCV to justify the smoothing parameter selection. When using locfit() with lp() I found that the gcvplot function does not work as it is looking for an alpha value to

Dear list members, I am trying to understand this output from the smoothing package locfit (1.5-4, running on R 2.8.1 on Windows Vista 64 bit). # sample code x<-1:100 y<-rnorm(100) fit<-locfit(y~x,family="gaussian") #default parameters are fine plot(fit,band="global") #plot seems "reasonable", confidence bands use a global estimate of variance

Dear R experts, We've been playing with the locfit package and are experiencing a problem I am hoping for some help on. We have a dataset of 13k points, one dimension of which is "judgment", either 0 or 1, and the other "score", an arbitrary scalar (in this case it's between .65 and .85). We use

Hi, I have a Q on locfit, E.g., I have an input data set of the form {(x, y, v)} , (x, y) is the location in a 2D space, v is the value at that location. I am wondering for the output of locfit(), if a given point (x, y) is in the input data set, is the value at (x, y) going to be the exactly same as the input? or it depends on the local fitting function? does my Q make sense? Thanks a lot in

Hi, I'm trying to work through the examples and code in Loader's LOCAL REGRESSION AND LIKELIHOOD, and have run into a problem with the code for one sided smoothing and change point analysis (p. 110-112). The code, after loading locfit: midp<-(1945:1988)+0.5 fitl<-locfit(thickness~left(year), data=penny, alpha=c(0,10), deg=1, ev=midp)