similar to: kernel density estimation with many variables (50 variables)

The new package "benchden" 1.0.0 implements 28 benchmark densities for nonparametric density estimation that were introduced by A. Berlinet and L. Devroye ("A Comparison of Kernel Density Estimates", Pub. Inst. Stat. Univ. Paris, XXXVIII, fasc. 3, 1994, 3-59, http://cg.scs.carleton.ca/~luc/devs.html ). This collection includes a variety of densities with different degrees of

The new package "benchden" 1.0.0 implements 28 benchmark densities for nonparametric density estimation that were introduced by A. Berlinet and L. Devroye ("A Comparison of Kernel Density Estimates", Pub. Inst. Stat. Univ. Paris, XXXVIII, fasc. 3, 1994, 3-59, http://cg.scs.carleton.ca/~luc/devs.html ). This collection includes a variety of densities with different degrees of

Hello I am using support vector machine (from package kernlab) for a classification task (with RBF-Kernel). My data has dozens of variables and I need to identify which variables contribute most to the classification performance. What I did so far is comparing the classification performance (measured for example with the proportion of misclassified cases) of different sets of variables with

Hi all, I am trying to estimate the mode of a 4-dimensional nonparametric density estimator (any) using a sample of size n=10,000. I have tried using the package 'ks' and 'np' but they are extremely slow; this is related to the estimation of the bandwidth matrix. I also checked the package 'modeest' but it contains only methods for univariate distributions. I am only

Hi! I have two dimensional dataset which has and I need to decide if a point lies in some "confidence level". If a point has low confidence/density it can be anomaly which I need to find. For example: #load library library(sm) #get some data x.locs = c(74, 74.5, 75, 77,74.5) y.locs = c(64, 63.5, 63, 61,61.5) points = cbind(x.locs, y.locs) #plot it plot(points) #get points density

Dear R experts, density estimation on a 2 dimensional bounded domain --------------------------------------------------------------------- I am currently trying to estimate the probability density (PD) of cancers within the breast using the sm library with the routine sm.density Of course a practical PD must be limited by the curve of the breast outline. I don't have a clue after perusing

Hi, I'd like to use a three-dimensional dataset to build a kernel density and then sample from the distribution. I already used the npudens function in the np package to estimate the density and plot it: fit<-npudens(~x+y+z) plot(fit) It takes some time but appears to work well. How can I use this to evaluate the fitted function at a certain point, e.g. (x=1, y=1, z=1)?

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:

Hi, I am wondering if the kde2d 2-D kernel density estimation function in the MASS package can take into account the effect of correlations between the variables. I couldn't find any achieved information on this issue. Unfortunately, I don't have the 2002 edition of Modern Applied Statistics with S by Venables and Ripley in case it was described there. Thanks in advance. Adam

Dear List, I am trying to estimate a 3 dimensional density through the DPpackage. For example # model sigma <- matrix(c(0.1,0.05,0.05,0.05,0.1,0.05,0.05,0.05,0.1), ncol=3) rnormm<- rmvnorm(n=100, mean=c(5,100,150), sigma=sigma) sigma2 <- matrix(c(10,0.05,0.05,0.05,10,0.05,0.05,0.05,10), ncol=3) rnormm2<- rmvnorm(n=100, mean=c(20,1,110), sigma=sigma) rnormm<-rbind(rnormm,rnormm2)

The sm package (by Adrian Bowman and Adelchi Azzalini) implements a variety of nonparametric smoothing techniques, centred on nonparametric regression for one or two covariates and density estimation for up to three variables. A new version of the package is now available on CRAN. In an earlier unannounced version (2.1), a variety of methods of bandwidth selection were added, with default

The sm package (by Adrian Bowman and Adelchi Azzalini) implements a variety of nonparametric smoothing techniques, centred on nonparametric regression for one or two covariates and density estimation for up to three variables. A new version of the package is now available on CRAN. In an earlier unannounced version (2.1), a variety of methods of bandwidth selection were added, with default

Hi, I am using the KernSmooth package to estimate nonparametrically bivariate density functions. However, it seems that the bandwidths (one for each co-ordinate direction) have to be selected manually. This does not apply for the univariate case, for which dpik (included in KernSmooth) uses up-to-date plug-in rules. Does anyone know about a package, or function, which estimates bandwidths

Hi, guys, I'm using kernel "density" estimation. But how can I return to a density estimation for a fixed point? For example, b<-runif(1000,0,1) f<-density(b) How can I get the value of density(b) at b=0.5? Your help is extremely appreciated. Thanks. Jay -- View this message in context: http://r.789695.n4.nabble.com/help-in-density-estimation-tp2552264p2552264.html Sent

Hey, Does anyone know of a package in R that provides univariate kernel density estimation with boundary correction ? or how to easily extend an existing bivariate kernel density estimation function (e.g. lambdahat in the spatialkernel package) with boundary corrections to allow univariate density estimation? Thanks a lot, Steve B. -- View this message in context:

Hello: I have been using R and the locfit package for Unix/Linux for a little while. However, I have had some trouble, as I am trying to do density estimation for bounded independent variables. There is some discussion in the density estimation book by Azzalini, but none in the book by C Loader (creator of locfit). Also, the variables that I am working on are bounded on both sides, not just

Dear all, I have a bivariate dataset from a preliminary study. I want to do two things: (1) estimate the probability density of this bivariate distribution using some nonparametric method (kernel, spline etc); (2) sample a big dataset from this bivariate distribution for a simulation study. Is there any good method or package I can use in R for my work? I don?t want parametric models like

Hello, my name is Carsten. This ist my first post to R-help mailing list. I estimate densities with the function "density" out of the package "stats". A simplified example: #generation of test data n=10 z = rnorm(n) #density estimation f=density(z,kernel="epanechnikov",n=n) #evaluation print(f$y[5]) Here I can only evaluate the estimation at given

Dear List, I have been trying to use library sm to do density estimation on a 3D dataset. I am using the current MacOS X binary of sm from CRAN. If I do this on a 2D dataset, sm.density returns a list including the component "estimate" which contains the density estimate over a uniform grid. When doing this with 3D data, although I get a nice plot (even when I don't ask for one),

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