similar to: Kernel distribution

Hi, Does anyone know if the D'agostino test is available with R ? Alex

Hi, in R there is the function "density" which computes kernel density estimates. Is there a "cumulative" version of it? Something like they have in Matlab: http://www.mathworks.nl/help/toolbox/stats/ksdensity.html I know there is ecdf, but I'm not sure it's based on kernel density smoothing. Thanks -- View this message in context:

Hi there, during my work I have to use kernel smoothing methods for multivariate ordinal data. The R-package "KernSmooth" unfortunately includes only a version for continous scaled variables. Does anybody know whether there exists also a version for ordinal data? Thanks for help! --

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 R users, I would like to show the estimated density of a (0, 1) uniformly distributed random variable. The density curve, however, goes beyond 0 and 1 because of the kernel smoothing. Example: x = runif(10000) plot(density(x)) Is there a way to estimate the density curve strictly within (0, 1) and still use some sort of smoothing? Any help would be greatly appreciated. Best regards,

Hello R users & R friends, I just want to ask you if density() can produce a confidence interval, indicating how "certain" the density() line follows the true frequency distribution based on the sample you feed into density(). I've heard of loess.predict(loess(y ~ x), se=TRUE) which gives you a SE estimate of the smoothed scatterplot - but density() kernel smoothing is not the

How can I plot the graph of a density of a sample with an unknown distribution? I can provide any sample size which is required. I want to have a smooth density graph of my data. New Email addresses available on Yahoo! Get the Email name you've always wanted on the new @ymail and @rocketmail. Hurry before someone else does! [[alternative HTML version deleted]]

Dear R community, I'm having hard time to understand the kde function in "ks" package. Let me use a 3-dimensional kernel smooth example to explain my question using the elevation data in geoR. ### here is what I did ### library(ks) require(geoR) data(elevation) elev.df <- data.frame(x = elevation$coords[,"x"], y = elevation$coords[,"y"], z = elevation$data)

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

Merry Xmas to all, I am writing a function and curiously this runs sometimes on one data set and fails on another and i cannot figure out why. Any help much appreciated. If i run the code below with data <- iris[ ,1:4] The code runs fine, but if i run on a large dataset i get the following error (showing data structures as matrix is large) > str(cluster.data) num [1:9985, 1:811] 0 0 0 0

I am trying to plot two density lines on the same graph. Using the functions on the base package, I would go: plot(density(x), col = 1) lines(density(y), col = 2) And I get two distinct (one-bump) density lines. When I try to do it using lattice, I get two two-humped lines. (In other words, I think the smoothing function is taking the next set of data points and smoothing them in the same

Hi, I want to use kde() or a similar function for kernel smoothing but I want to specify the weight of each of my data points. I do not want to specify the bandwidth on a point by point basis. This seems such a simple and obvious thing to want to do I am suspicious that there is not an obvious way to do it. The only discussion I have found is about negative weights(!) and says nothing

Hi all, I am using the vegan package to run a prcincipal components analysis on forest structural variables (tree density, basal area, average height, regeneration density) in R. However, I could not find out how to extract factor loadings (correlations of each variable with each pca axis), as is straightforwar in princomp. Do anyone know how to do that? Moreover, do anyone knows

I have some data measured with a coarsely-quantized clock. Let's say the real data are q<- sort(rexp(100,.5)) The quantized form is floor(q), so a simple quantile plot of one against the other can be calculated using: plot(q,type="l"); points(floor(q),col="red") which of course shows the characteristic stair-step. I would like to smooth the quantized

I am using density() to plot a density curves. However, one of my variables is truncated at zero, but has most of its density around zero. I would like to know how to plot this with the density function. The problem is that if I do this the regular way density(), values near zero automatically get a very low value because there are no observed values below zero. Furthermore there is some density

Hello to everyone I''ve downloaded the version sm2 for smoothing methods and when I try the simple code y <- rnorm(50) sm.density(y, model = "Normal") I get the error message Error in if (any(omit)) { : missing value where logical needed I''m running R 1.1.1. on windows 98 Anybody can help? -------------- next part -------------- An HTML attachment was

Hello, I faced a problem with rsync-ing like this server blue 3-HD's as Linux Sofware-RAID 0 (striping) - Webserver EXT3 server green 3-HD's as Linux Sofware-RAID 0 (striping) - Backupserver EXT3 During the rsync-2.4.6-13 process one of the HD's on blue died. Though rsync decided to remove the mirrored directories from green (the backup-server). This process resulted in a partial

Dear fellows, Thank you. Our company engaged in developing Voice conference sofware.Now we are using speex 1.0.5 acm for our sofware.And we are using VB6 for our develpement. We would like to know anybody is intrested in developing VC++ dll which could export encode decode of speex to vb so that we can use it VB6.Our clients would be pleased to invest money for it. thank you regards

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

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