similar to: bivariate weighted kernel density estimator

Dear R users: I have a dataset with two variables (>20000 observations, two samples from same subject) and I used "kernSur" from library(Genkern) to get a estimated bivariate density and corresponding plots as follows: new.data.normal<-data.normal[!is.na(data.normal[,2]),] x<-new.data.normal[,2] y<-new.data.normal[,3] op <- KernSur(x,y, xgridsize=50, ygridsize=50,

Hi Spencer, I have seen your name on the web site, and perhaps you can help me with my R problem. I'm trying to use KernSur to put in evidence a substructure in a bidimensional plot. My problem is that, in order to get the density in the low density areas (in which the substructure is located) I should use different bandwidths. How I can do that? Also, I think that the best choice for my

Hi all I'm trying to create a three-dimensional wireframe-type plot, much like the screenshot shown at http://www.r-project.org/screenshots/volcano-persp.jpg In my case, I'm trying to show a density estimate of two-variable system, using the GenKern package. I've used the image() and contour() to create a nice shaded plot, but I've found a 3d plot is often easier for the

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'm trying to take a data set with two independent and one dependent variable and enter a x,y value to predict the dependent with a nonparametric technique. I've been using interpp in the akima package, (windows xp, R 2.4.1), but get values that are orders of magnitude off when the predictors are slightly out of the range of the data set. Can you recommend a function for me?

Hi, there. Does anyone know the R function for calculating the cdf of bivariate normal distribution function? Thanks. Yulei [[alternative HTML version deleted]]

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 have written the following function to calculate the weighted mean difference for univariate data (see http://www.xycoon.com/gini_mean_difference.htm for a related formula). Unsurprisingly, the function is slow (compared to sd or mad) for long vectors. I wonder if there's a way to make the function faster, short of creating an external C function. Thanks very much for your advice. gmd

Dear all, I created a bivariate normal distribution: set.seed(138813) n<-100 x<-rnorm(n); y<-rnorm(n) and plotted a scatterplot of it: plot(x,y) Now I'd like to add the 2D-standard deviation. I found a thread regarding plotting arbitrary confidence boundaries from Pascal H?nggi http://www.mail-archive.com/r-help at r-project.org/msg24013.html which cites the even older thread

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

In the document "R Data Import/Export", section "Output to connections", there is the following portion of code: ## convert decimal point to comma in output, using a pipe (Unix) zz <- pipe(paste("sed s/\\./,/ >", "outfile"), "w") cat(format(round(rnorm(100), 4)), sep = "\n", file = zz) close(zz) ## now look at the output

There seems to exist peculiar cases where optim does not take care of constraints on the parameters to be optimized over. The call to optim is of the form opt <- optim(cp, fn=sn.dev, gr=sn.dev.gh, method="L-BFGS-B", lower=c(-Inf, 1e-10, -0.99527), upper=c( Inf, Inf, 0.99527), control=control, X=X, y=y, hessian=FALSE) The code has worked fine

Dear R-help list members, I have created a package 'mnormt' with facilities for the multivariate normal and t distributions. The core part is simply an interface to Fortran routines by Alan Genz for computing the integral of two densities over rectangular regions, using an adaptive integration method. Other R functions compute densities and generate random numbers. The starting

I have justed upgraded R v1.7.0 on Windows NT 4 and have installed the latest RMySQL (version 0.5-1)and DBI (version 0.1-5) packages. When I issue the following commands (tactfully adjusted) R just crashes and disappears, any ideas? require(RMySQL) m <- dbDriver("MySQL") con <- dbConnect(m, dbname="xxx", user="xxx", password="xxx",

Hi all, I'd like to quantize a variable to map it into a limited set of integers for use with a colormap. "image" and filled.contour" do this mapping inside somewhere, but I'd like to choose the colors for plotting a set of polygons. Is there a pre-existing function that does something like this well? i.e., is capable of using 'breaks'?

Hello, How can I obtain the likelihood ratio of a Poisson regression model? Regards. _____________________________________________ dr. Marziliano Ciro Facolta' di Economia Universita' degli Studi di L'Aquila p.zza del Santuario, 19 67040 Roio Poggio, L'Aquila tel.: 0862 434836 fax: 0862 434803

I am learning glm function, but how do you fit data using exponential distribution with glm? In the help file, under "Family Objects for Models", no ready made option seems available for the distribution as well as for other distributions satisfying GLM requirements not listed there.

Dear list, Does anyone know how to fit the power law distribution? I have the empirical distribution and would like to check whether it fits the power law (with the power estimated from the data). Any hints are appreciated. Tanks a lot! Galina [[alternative HTML version deleted]]

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)

Hi R-users,  I have a data from multi skew t and would like to transform each of the data to uniform data.  I tried using 'pmst' but only got one output:   > rr1 <- as.vector(r1);rr1  [1]  0.7207582  5.2250906  1.7422237  0.5677233  0.7473555 -0.6020626 -2.1947872 -1.1128313 -0.6587316 -1.1409261     > pmst(rr1, xi=rep(0,10), Omega=diag(10), alpha=rep(1,10), df=5) [1] 3.676525e-09