similar to: Sampling from multi-dimensional kernel density estimation

Hi List, I'm trying to get a density estimate for a point of interest from an npudens object created for a sample of points. I'm working with 4 variables in total (3 continuous and 1 unordered discrete - the discrete variable is the character column in training.csv). When I try to evaluate the density for a point that was not used in the training dataset, and when I extract the fitted

Hi, I am looking for some help concerning the npudens function in the np package. I am trying to find a kernel density function of a multivariate dataset and the density evaluated at each of the 176 points. I have 2 continuous and 3 ordered discrete variables. My sample size is 176. So edata is a 176x(2+3) data frame, while tdat is a 1x(2+3) vector. bw_cx[i,] is a 1x (2+3) vector

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 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, 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

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]]

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

(disclaimer: I'm in physics, not stats... ) I have a multivariate problem. One variable, call it R1, and 3 "predictor" variables, P1, P2, P3. My goal is to take a load of training data (I know R1,P1,P2,P3 for about 700 total points), and then predict R1 for a new set of data for which I have all the predictors. Simple, no? I understand how to calculate bandwidths, and I have a

Dear all I would like to perform density estimation with a weighted sample (output of an Importance Sampling procedure) in R. Could anybody give me an advice on what function to use (in which package)? Thanks a lot, Lorenzo

Hi all, I want to use the npudens() function in the np package (multivariate kernel density estimation), but was confused by the several functions in the following codes,expand.grid(),array(),image() and npudensbw(). This confusion will only be generated in >=3 dimensions. I marked the four places with confusion1-4. I think there should be some kind of correspondence in those four

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

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

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

Hi All, I have a sample x={x1,x2,..,xn} fom a distribution with density f. I wish to estimate the density. I know a priori that the density is monotonically decreasing. Is there a way to do this in R? Thanks Debayan

Hi, I've estimated a simple kernel density of a univariate variable with density(), but after I would like to find out the CDF at specific values. How can I do it? thanks for your help, with it I am very close to finish my first little bit more serious work in R, Viktor

I am a physician examining an NHANES dataset available at the NCHS website: http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/demo_d.xpt http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/hdl_d.xpt http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/tchol_d.xpt Thank you to the R authors and the foreign package authors in particular. Importing from the SAS export

Dear all:Request your indulgence. The econophysics gurus do this stuff all the time: all their PDFs are smooth, with neat log x axis. 1. The kernel density estimate (KDE) function returns the empirical probability density at 2^n points (min: 512). The big question is how do I scale back the x-values (say, density$x) to x-values in terms of the original dataset? 2. To give you a concrete idea, i

Hi, I am trying to plot densities given on a two dimensional grid. My data is in the an external file, and is arranged in three columns: x, y, density how may i get a plot of this? i would like to get (1) a three dimensional plot and (2) a color coded two dimensional plot. I have tried using image(x, y, density) but i am asked to put the data in ascending order. i am not sure how i may

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

Dear All, I was wondering whether someone has created a kernel density evaluator that estimates the density at given specified points. The regular density() function evaluates the kernel at equidistant points, but I am interested in doing such evaluation along a list of values existing in a pre-specified vector. (Similar to the option at() in the kdensity command in Stata). This question has