similar to: 3D density estimation

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

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

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

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

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]]

I have 3 row data in the format as x,y,time I want to draw it as a 3d picture;x as x axis, y as y axis, the xy's density as z axis, the corresponding time as color. I have read the manual of R and failed, Since kde2d and bkde2d give grid data, this grid data cannot match its original time. Could anyone do me a favor? thanks.

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:

Hello, I have a n*2 matrix, called "plan", which contains n observations from 2 variates. I want a kernel density estimate of the joint distribution of these 2 variates. I try : density(plan). Unfortunately, R thinks there is 2n observations (if n=10, 20 observations), where there is only n. How to to make a multivariate kernel density estimate ? Thank you very much.

Dears, I've been learning biplot (Gabriel, 1971) and I found the function 'biplot', inside of the package 'stats', useful but, a bit limited. So, I'm thinking to start a colaborative package to enhance this methods to other multivariate methods. In this way, I would like to start it, making public a new function (biplot.pca, still in development, but running) that make

Dear all, a colleague of mine has some code that calls "linbin2D" in package "KernSmooth" directly. That code is broken under R 1.7.0 since "KernSmooth" does not export "linbin2D" from its namespace. Via trial and error we found to possible solutions: 1) Edit the file NAMESPACE in the directory where KernSmooth is installed such that linbin2D is

Dear wizaRds, I use the MASS::kde2d function to estimate density of the two first principal components. I do that to have a graphic visualisation of a "group structure" in my dataset. So far, no problem. But i would like to estimate that density using weights according to the COS?? values that tells me if my observation is well represented on the factorial plan 1-2. I would like to

I've been trying to implement bivariate kernel density estimation. For data like mine, function "kde" from package "ks" with bandwidth matrix derived by function "Hscv" seems like a very good choice. Unfortunately, Hscv seems unmanageably slow except for very small sample sizes (up to a few hundred) and my sample sizes are quite large (up to a few thousand).

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

Estimate region of highest probabilty density Dear R-community I have data consisting of x and y. To each pair (x,y) a z value (weight) is assigned. With kde2d I can estimate the densities on a regular grid and based on this make a contour plot (not considering the z-values). According to an earlier post in the list I adjusted the kde2d to kde2d.weighted (see code below) to estimate the

Hallo, I want to use kde2d to visualize data on a sphere given in spherical coordinates. Now the problem is, that "phi == 2*pi = 0", so in principal I have to connect (in a graphical view) the left and right border of my plot (and the bottom and top). Has anyone any idea how to do that ? Thanks, Manuel -- ------------------------------------- Manuel Metz Sternwarte der

Is there code for bivariate kernel density estimation? For bivariate kernels there is kde2d in MASS kde2d.g in GRASS KernSur in GenKern (list probably incomplete) but none of them seems to accept a weight parameter (like density does since R 2.2.0) -- Erich Neuwirth, University of Vienna Faculty of Computer Science Computer Supported Didactics Working Group Visit our SunSITE at

Dear listers, After activating the name space for my bioconductor package (prada) I successfully ran R CMD check. However when loading the package in R and running the examples the imported function brewer.pal from package RColorBrewer is not found. I can directly call brewer.pal from the RColorBrewer name space typing RColorBrewer::brewer.pal, but it is not imported into my prada name space. When

Dear listers, After activating the name space for my bioconductor package (prada) I successfully ran R CMD check. However when loading the package in R and running the examples the imported function brewer.pal from package RColorBrewer is not found. I can directly call brewer.pal from the RColorBrewer name space typing RColorBrewer::brewer.pal, but it is not imported into my prada name space. When

Hi, I guess I have a naive question. I use kde2d function in a standard way to estimate kernel densities of x and y (x and y are vectors) and plot them using image(). f1=kde2d(x,y) image(f1) But what if I want to see kernel estimates of three variables, x, y and z (a vector) plotted together ? Something in which x<->y is plotted and colored according to the corresponding value of z ?

Dear List, I don't get scatter3d to color the sheres according to the '|' argument. library(car) scatter3d(prestige ~ income + education|type, data=Prestige) The spheres on my screen are all colored the same and they are not conditional on Prestige$type. On the other hand: Fit3d and Ellipse3d are colored according to the group argument. rgl_0.92.879 car_2.0-12 R version 2.15.0