search for: kde2d

Good evening, I am Marta Colombo, student at Milan's Politecnico. Thank you very much for your kindness, this mailing list is really useful. I am using the function kde2d for two-dimensional kernel density estimation and I'd like to know something more about this kind of density estimator. In particular I'd like to know: what bandwidth is used ? Thank you in advance for your attention Marta Colombo

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 <- c...

Hello! I am trying to do a smooth with the kde2d function, and I'm getting an error message about NAs. Does anyone have any suggestions? Does this function not do well with NAs in general? fit <- kde2d(X, Y, n=100,lims=c(range(X),range(Y))) Error in if (from == to || length.out < 2) by <- 1 : missing value where TRUE/FALS...

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...

..., I'd be very grateful for help with some ggplot2's stat_density2d issues. First issue is with data limits. xlim() and ylim() doesn't seem to work; instead, estimates (and plotting) seems to be constrained to range(x), range(y) no matter what i do. The documentation says i can pass in kde2d's parameters to ... but pussing kde2d's "lims" parameter achieves nothing. Contrary to the installed package help, the ggplot2 site reference says that "..." parameters are ignored. So does it or does it not use kde2d? it seems not, but if not, how do i set the limit? s...

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 densities of x and y considering their weights z. There's also a...

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 -- ---...

Dear R users, I used sm.density function in the sm package and kde2d() in the MASS package to estimate the bivariate density. Then I calculated the Kullback leibler divergence meassure between a distribution and the each of the estimated densities, but the asnwers are different. Is there any difference between the kde2d and sm.density estimates? if there is a differ...

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 http://s...

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, The silverman's paper introduction offer how to find a mode for one dimensional data based on software http://www.stanford.edu/~kasparr/software/silverman.r, for two dimensional data I use kde2d to smooth it out first, then I get a matrix of densities for all the X(one dimension) cross Y(another dimension). I sort X and Y first before I pass the values to kde2d(x, y, c(hx, hy)), the persp shape changes Does anyone know how to get the modes out of the two dimensional data programmatical...

Hello I'm trying to calculate 2d confindence bounds into a scatterplot using the function "kde2d" (package MASS) and a contour plot. I found a similar post providing a solution - unfortunatly I do not realy understand which data I have to use to calculated the named "quantile": Post URL: http://tolstoy.newcastle.edu.au/R/help/03b/5384.html > (...) > >> Is there a...

...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 the function kde2d(), and plotted with persp(). Thanks a bunch. -Joe [[alternative HTML version deleted]]

Hi, I'd like to partition a 2d probability density function into regions of equal probability. It is straightforward in the 1d case, like qnorm(seq(0,1,length=5)) but for 2d I'd need more constraints. Any suggestions for how to approach this? Is seems like a spatial sampling problem but I'm not sure where to look. Thanks for your time, Dave -- Dr. David Forrest drf at

Hello, I used the following codes to generate bivariate normal dependence structure with unit Frechet margins. Sigma <- matrix(c(1,.5*sqrt(1),.5*sqrt(1),1),2,2) # generate y <- mvrnorm(Nsam, c(0,0), Sigma) # random v <- cbind(pnorm(y[,1],mean = 0, sd = 1), pnorm(y[,2],mean = 0, sd = 1)) z <- cbind(-1/log(v[,1]),-1/log(v[,2])) z1 <- z[,1] z2 <- z[,2] And to

Hi, Does anyone know how to count the number of modes in 2 dimensions using kde2d function? Thanks Pat

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 color...

...r such data? I found this article http://www.dmqte.unich.it/personal/dimarzio/density46.pdf but a) I don't fully understand the article (my knowledge in statistics is poor) b) I did not understand which Eq. represents the kernel(s) c) I do not now R well enough to understand whether I can use kde2d or nprudens with an arbitrary kernel My simple-minded attempt was to extend the data to [-2pi:4pi) and then use kde2d, but I am wondering a) how accurate is this b) is there a way to do it properly on a torus. Thanks a lot, Tim -- -- Tim Gruene Institut fuer anorganische Chemie Tammannstr. 4...

Hi, I'm unable to find a way to smooth data for a persp() graph. Example, suppose that I have data x,y,z like this: x <- 1:10 y <- 1:10 k <- 20 z <- outer(x, y, "*") + matrix( k*runif(100, -1, 1), 10, 10) persp(x, y, z, theta = 35, phi = 25) The graph is not very nice. Is there a way to smooth the z data so that at the end the graph would look more like something

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...