similar to: Weighed 2D kernel density estimator

Hi all, Do any packages implement density estimation in a modelling framework? I want to be able to do something like: dmodel <- density(~ a + b, data = mydata) predict(dmodel, newdata) This isn't how sm or KernSmooth or base density estimation works. Are there other packages that do density estimation? Or is there some reason that this is a bad idea. Hadley -- http://had.co.nz/

Dear All, I am researching financial market microstructure and have approx 4 x 10^7 multivariate 2D data samples which I have counted into a 250 x 390 bin matrix (frequency counted 2D histogram) in order to more efficiently manage the volume of data. I now wish to construct a smooth kernel density estimate (Gaussian kernel function) using this binned data. Does anyone in the R community know

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

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

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

Hi all We are pretty new to R here and trying to achieve something that we believe is possible but it?s not easy to work out how to do it. We are producing scatterplot matrices for e.g. 10 variables. What we would like to do is superimpose 2D kernel density estimators on top of each plot so that we end up with a scatterplot matrix of 2D kernel density (contour) plots. The kernel density plots

Hello... Is it possible to use "density" or another kernel density estimator to identify the mode of a distribution? When I use 'density', the resulting density plot of my data is much cleaner than the original noisy histogram, and I can clearly see the signal that I am interested in. E.g., suppose my data is actually drawn from two or more normal (or other)

This might be of interest: http://math.usu.edu/~minnotte/research/software/modetree.r (I was not able to get to the link, but google has a cached version.) Prof. Marron's SiZer maps may also be of interest, but AFAIK the code is in Matlab only. Andy From: Dan Rabosky > > > Hello... > > Is it possible to use "density" or another kernel density > estimator to

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

Hello R users, I have successfully created a square (or more generally, rectangular) tophat smoothing routine based on altering the already available KDE2D. I would be keen to implement a circular tophat routine also, however this appears to be much more difficult to write efficiently (I have a routine, but it's very slow). I tried to create one based on using crossdist to create a distance

Hi I have some density estimates obtained from density(). I would like to calculate the sum of squares of these. As the x values of the estimates are not the same, and I would prefer not to restrict the estiomate to a certain range of x values, how can I do the calculation? Lets say: d1 <- density(Data1) d2 <- density(Data2) If the x values would be the same, I would: ssq <- sum(

Dear R-helpers, I want to smooth a scatter plot (or 2d histogram) to generate the density map using a squared Euclidean kernel function, with each data point contributing a density of 1/(r*r + k) to each cell on the heatmap, where r*r?was the squared Euclidean distance between the coordinates of the cell and the coordinates of the point, and k was a constant smoothing factor. There is figure to

For the hist estimate >par(mex=1.3) >dens<-density(q) >options(scipen=4) > ylim<-range(dens$y) > h<-hist(q,breaks="scott",freq=FALSE,probability=TRUE, +? right=FALSE,xlim=c(9000,16000),ylim=ylim,main="Histogram of q(scott)") > lines(dens) >box() ? For the kernel estimate>options(scipen=4) > d <- density(q, bw =

You can try `locfit', though it does local likelihood, rather than garden-variety kernel density estimation. Here's an example: library(locfit) data(cldem) den.fit <- locfit(~ x1 + x2, data=cltrain) predict(den.fit, newdata=cltrain) Andy From: Strickland, Matthew > > Hi, > > My data consists of a set of point locations (x,y). > > I would like to know if there

Dear All, Attached are the codes of a histogram & a kernel density estimate and the output they produced. In fact the variable q is a vector of 1000 simulated values; that is I generated 1000 samples from the pareto distribution, from each sample I calculated the value of q ( a certain fn in the sample observations), and thus I was left with 1000 values of q and I don't know the

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),

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

I have two series of events over time and I want to construct a graph of the relative frequency/density of these events that allows their distributions to be sensibly compared. The events are the milestones items in my project on milestones in the history of data visualization [1], and I want to compare trends in Europe vs. North America. I decided to use a graph of two overlaid 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