similar to: How to get the datapoints of an density estimationwithlocfit?

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.

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

It is known that function bkde2D (package "KernSmooth") returns a matrix of density estimates over the mesh induced by x1 and x2. In Details it is written that "... heights of the kernel, scaled by the bandwidths, at each datapoint are summed. This sum, after a normalization, is the corresponding fhat value in the output". There are several questions: 1) How to calculate

Hi! I have a problem with Kernel density estimate plot for 3-dimensional data in ks-package. Here the example: # load ks, spatstat # three-dimensional kernel density of B B <- pp3(runif(300), runif(300), runif(300), box3(c(0,1))) x <- unclass(B$data)$df H <- Hpi(x) fhat <- kde(x, H=H) plot(fhat) plot(fhat, axes=FALSE, box=FALSE, drawpoints=TRUE);

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

I am trying to reproduce Figure 10.5 of Loader's book: Local Regression and Likelihood. The code provided in the book does not seem to work. I have managed (a while ago) to get the accompanied R-code for the figures in the book (file called lffigs.R) from somewhere - cannot find it on the web anymore. The code in the .R script file does not work either. Could anybody please direct me in

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 <-

Hallo I am trying to use the package LocFit to follow the example given in an Introductory note of C Loader concerning density estimation. It involves the "geyser" dataset (107 observations on durations, inlc in the package). I have tried the following (using the latest version of R): fit.of <- locfit(~geyser,flim=c(1,6),alpha=c(0.15,0.9)) plot(fit.of,get.data=T,mpv=200) This

I'm trying to make a set of contour plots of bivariate kernel density estimates, showing three such plots overlaid, similar to this plot http://euclid.psych.yorku.ca/SCS/Private/Test/ridge-boot2.pdf except that I would like to have the contours *filled* (using transparent colors). To make this reproducible, I've saved the results of KernSmooth::bkde2D() in the following file:

I have spatial data in 2 dimensions - say (x,y). The correlation between x and y is fairly substantial. My goal is to use a non-parametric approach to estimate the multivariate density describing the spatial locations. Ultimately, I would like to use this estimated density to determine the area associated with a 95% probability contour for the data. Given the strong correlation between x and

Dear R-devel, I'm working on Prof. Loader's new version of locfit to try to get it pass R CMD check. I'm almost there, but I have a problem with some Rd files that I hope some one can help me resolve. Here's an example: In the package there's a function called locfit.censor(). This function can be used in a few different ways: locfit.censor(x, y, cens, ...)

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

Hello, I am a newbie to R and the subject of density estimation in two dimensions or more. I would like to have some advice concerning a comparison between the R packages for density estimation in bivariate or higher order problems; I mean explicitly the packages: 1) ash 2) KernSmooth 3) locfit 4) sm. My specific problem now is having a set of numerical pairs (x_i, y_i), arising from a

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

Dear R users, i am using locfit package developed by loader in R software, my problem is that as i am doing independont forecast using locfit object , i am able to do independont forecast for more than one years simultaniously. But when i am doing one year forecast(single) this code is giving following error... "Warning message: 'newdata' had 1 rows but variable(s) found have 24

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

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

Dear R-users, One of the main reasons I moved from GAUSS to R (as an econometrician) was because of the existence of the library LOCFIT for local polynomial regression. While doing some checking between my former `GAUSS code' and my new `R code', I came to realize LOCFIT is not quite doing what I want. I wrote the following example script:

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