similar to: Density estimates in modelling framework

Hi All: Package TSERIES is stated to depend on MVA. However, there is no MVA package to be found under the list of package sources. Best wishes, ANDREW tseries: Package for time series analysis Package for time series analysis with emphasis on non-linear and non-stationary modelling Version: 0.7-6 Depends: ts, mva, quadprog Date: 2001-08-27 Author: Compiled by Adrian

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 all, I'm reposting this with a more appropriate subject. Do I need to define limits as the error message seems to suggest? If so, how? The error message, my code, the output and the first few lines of my data are all below. Thank you! "Error in Getlim(at, allow.null = TRUE, need.all = TRUE) : variable dmodel.df does not have limits defined in fit or with datadist" My code:

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

Hello!! At first thank you all for your suggestions and your help. But I would like to get another group of datas from the density estimation. I don't know if these information are provided by locfit or I should use an other function like plot(). Description: Each number between 1 and 100 (only integer) for example should have only one density value. But I also would like to have the density

Hello, I have a data x with normal (or very close to normal) distribution, I can plot a density distribution with density(x,...). My question is is there any way to calculate an area under this distribution (=probability) for particular range of x values, let's say for x from 0 to 2? I was not able to find any kind of simple procedure to do this. Thanks in advance for your help, Evgeny.

Hi, I found that the R package "KernSmooth" can deal with only 1D and 2D data. But now I have a collection of 4-dimensional data (x1,x2,x3,x4) and would like to estimate the "mode" of the underlying density. What can I do about it ? Thanks a lot. -- Ying-Chao Hung Assistant Professor Graduate Institute of Statistics National Central University Chung-Li, Taiwan TEL:

I am trying to carry out density estimation for three dimensions (with anywhere between a few hundred and ~8000 data points). Which leads me to ask: a) is there any equivalent to kde2d (in MASS) or bkde2D (in KernSmooth) out there for three dimensions? b) if not, my skills only seem to extend as far as writing a function which measures density as the number of data points falling within a

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, usually I work under linux, but I tried to install R-1.1.1 on our alpha's which worked well. However, installation of the locfit package produced the following warning: ---------%<---------------- Installing package `locfit' ... libs /vol/bin/gcc -I/vol/math/R/include -I/usr/local/include -mieee -g -O2 -c S_e nter.c -o S_enter.o S_enter.c: In function `basis':

Hello, I wanted to know if there is a simple way of getting the inverse cdf for a KDE estimate of a density (using the ks or KernSmooth packages) in R ? The method I'm using now is to perform a numerical integration of the pdf to get the cdf and then doing a search for the desired probablity value, which is highly inefficient and very slow. Thanks, -fj [[alternative HTML version deleted]]

Dear R-list, I have made a simple kernel density estimation by x <- c(2,1,3,2,3,0,4,5,10,11,12,11,10) kde <- density(x,n=100) Now I would like to know the estimated probability that a new observation falls into the interval 0<x<3. How can I integrate over the corresponding interval? In several R-packages for kernel density estimation I did not found a corresponding function. I

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

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]]

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've been trying to figure this out for a while, but my knowledge of R is obviously still too limited. The context is as follows: I have some time series, and I would like to estimate their densities, and then use the actual densities in a monte carlo simulation. Now, I can easily estimate the density using density(); I can write a random number generator to fit an arbitrary density

Hi folks, I know that density function will give a estimated density for a give dataset. Now from that I want to have a percentage estimation for a certain range. For examle: > y = density(c(-20,rep(0,98),20)) > plot(y, xlim=c(-4,4)) Now if I want to know the percentage of data lying in (-20,2). Basically it should be the area of the curve from -20 to 2. Anybody knows a simple

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 R-users, I intend to do a spatial analysis on the genetic structuring within a population. For this I had thought to prepare a kernel density estimate map showing the spatial distribution of individuals, while incorporating the genetic distances among individuals. I have a dataset of locations of N unique individuals (XY-coordinates) and an NxN matrix with the genetic distances given as a

Dear Sir/Madam; Would you please tell me what is the command that allows the estimation of the Kernel Density for some data. Thanks, Thami Rachidi [[alternative HTML version deleted]]