similar to: kde2d and borders

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/FALSE needed Thanks in advance!! Jen [[alternative

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 <-

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

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

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 difference, then which is the best

Dearest list, I am using dovecot with heartbeat-2 and I noticed that the init scripts in the Debian packages from http://xi.rename-it.nl/debian/ do not conform to the LSB standard: http://refspecs.freestandards.org/LSB_3.1.1/LSB-Core-generic/LSB-Core-generic/iniscrptact.html E.g. the return code for "program is not running" is not 3. Is there is special reason for this? Cheers Jan --

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

Hi list, I get these warnings quite frequently. Warning: Failed to do incremental sync for mailbox INBOX, retry with a full sync Is this something to worry about? Otherwise the replication works perfectly. Cheers Jan -- MAX-PLANCK-INSTITUT fuer Radioastronomie Jan Behrend - Rechenzentrum ---------------------------------------- Auf dem Huegel 69, D-53121 Bonn

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 list, I have a replicated dovecot with two servers. People seem to get vacation responses more often then it is specified in "days: XX", depending on which dovecot instance they receive the incoming mail. Is ".dovecot.lda-dupes" replicated? The other problem is that the vacation response is coming from the wrong address depending on which field the recipient was

Dearest list, just now I migrated a pine user to dovecot. Nothing special so far, but then I saw this in my syslog and the user reported, that he got this error message: MAIL FOLDER "INBOX" CLOSED DUE TO ACCESS ERROR Obviously this problem is related to his local filter rules: If commented out everything works like a charm. The corresponding filter rule looks for a

Hello, 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

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

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

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

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

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 ?

Hello, I have 2D-data on a torus, i.e. they are scattered within [0:2pi) and are supposed to be periodic with period 2pi. Is there a way in R for a kernel density estimation for 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.

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

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 in case it was described there. Thanks in advance. Adam