similar to: Cross-correlated variables in kernel density estimation

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

Hello, I am a little bit confused regarding the density values obtained from the function kde2d() from the package MASS because the are not in the intervall [0,1] as I would expect them to be. Here is an example: x <- c(0.0036,0.0088,0.0042,0.0022,-0.0013,0.0007,0.0028,-0.0028,0.0019,0.0026,-0.0029,-0.0081,-0.0024,0.0090,0.0088,0.0038,0.0022,0.0068,0.0089,-0.0015,-0.0062,0.0066) y <-

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

Problem: Based on a n x 2 data matrix i want a kernel estimate of the bivariate density. However, i also wish to specify wich points the density should be calculated at. I can offcourse just write the full kernel density estimate as a R-code, but surely there must already exist some package for this operation? The package density(), seems to create a new matrix (depending on n), where the

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 fairly new to R, so I'm still trying to feel out what is available to me. I would like to be able to plot joint density in a two dimensional plot where density is indicated by color or darkness gradients, like a 2d color coded topographic map. Ideally, the output would be something I could then plot other points or lines on. Currently, I'm calculating joint density with

Dear all, Please, look at the following code: attach(geyser) f1 <- kde2d(duration, waiting, n = 5) a <- 0 for (i in 1:5){ for (j in 1:5){ a <- a + f1$z[i,j] } } As far as I understood from Help kde2d returns matrix elements of which are values of joint probability mass function Pr(X=x,Y=y) therefore, sum of its elements should sum to 1. Which is not the case from my check. Where is

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

Hello; I wrote this simple loop pass to collect the number of instructions in each loop of the program. The code is as follows- #define DEBUG_TYPE "loopinst" #include "llvm/Pass.h" #include "llvm/Analysis/LoopPass.h" #include "llvm/Support/raw_ostream.h" #include "llvm/ADT/Statistic.h" #include "llvm/Instructions.h" #include

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, > Problem solved. I was building llvm in a separate llvm-build directory. I > built it within the llvm-src directory (which kept all the llvm .so and my > pass' .so in the llvm-src/Release+Asserts/lib directory) to solve the > problem. I do not fully understand what you mean, there should be no difference on building out of source AFAIK. > Can anyone tell me what's

Problem solved. I was building llvm in a separate llvm-build directory. I built it within the llvm-src directory (which kept all the llvm .so and my pass' .so in the llvm-src/Release+Asserts/lib directory) to solve the problem. Can anyone tell me what's the difference between writing a pass as a "struct" (as in the tutorial) and as a "class" (as most developers do)?

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, This sounds like a problem to which many solutions should exist, but I did not manage to find one. Basically, given a list of datapoints, I'd like to keep those within the X% percentile highest density. That would be equivalent to retain only points within a given line of a contour plot. Thanks to anybody who could let me know which function I could use! Best, Emmanuel

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 ?

Hey, folk. I am trying to get many figures by using the function "xyplot" in the library "lattice". I tried to using the loop to finish it quickly. But I cannot open the saved file after I run the program. But if I use the function "plot" to get other simply figures, it can work. So I want to ask how I can get the saved figures by using "xyplot". The

Hello; I wanted to insert two new terminator instructions in LLVM IR. I modified the necessary files (as mentioned in the tutorial in LLVM site and some more to meet the new structure of LLVM, (e.g no Lexer.l now, we have LLLexer.cpp. Again in the tutorial, files like LLVMBitCodes.h, include/llvm-c/core.h that need to be modified are not mentioned) But I am getting the error while building

Hello all; So here is my goal: *** If there is a Call instruction to myFunction(int * val), I need to identify all the instructions that uses val in the IR and replace the uses with a newly created pointer. I will also be changing the argument of myFunction to the new pointer. int * val = malloc/calloc; ... myFunction(val); .... *val = 45; becomes== int * val = malloc/calloc; int * val1 =

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]]

hello sorry for my english I would like estimate density for multivariate variable,( f(x,y) , f(x,y ,z) for example) ; for calculate mutual information how is posible with R? thanks Bernard Bernard Palagos Unité Mixte de Recherche Cemagref - Agro.M - CIRAD Information et Technologie pour les Agro-Procédés Cemagref - BP 5095 34033 MONTPELLIER Cedex 1 France