similar to: confidence bounds using contour plot

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

Here''s a neat trick for a machine running kerneld: not_root@machine$ /sbin/ifconfig isofs loads ''/lib/modules/(kernel version here)/fs/isofs.o''. /sbin/ifconfig when run as non-root queries a network interface for its configuration. However, if the interface is unknown it also tries to load the module that implements that interface using the name of the interface as

Dear List, somebody knows, if the following operation can be done in an easier way? The data rows which should be read into R look like that: 4547;1970.01.01 00:00-1970.01.01 01:00; noData Unitil now we are doing this procedure: 1. Dividing columns which are separated by ";" => temp <- read.table(file ,sep=';', na.strings='noData', strip.white=TRUE) =>

Dear list, I have to look examine hourly time - series and would like to plot variable section of them using plot.zoo. Hourly time series data which looks like this: YYYY MM DD HH P-uk P-kor P-SME EPOT EREA RO R1 R2 RGES S-SNO SI SSM SUZ SLZ 2003 1 1 1 0.385 0.456 0.021 0.000 0.000 0.000 0.013 0.223 0.235 0.01 0.38

Dear R-users I would like to draw a barplot with a special y-axis. Most of my data points are in the range from -50 to 50, apart from one value that is 550. I would now like to have a y-axis from -50 to 50, then a "break" and the "rest" of the y-axis, let's say from 500 to 600. In order to make clear what I'm trying to do, I attached a figure. Is it possible to do

dear R-community, Is it possible to have one pie chart of a large radius and then a pie chart with smaller radius placed on top of the larger so the centers of the two pie charts coincide? I managed to place a filled circle with smaller radius in the piechart, see below. Thanks in advance, Sybille pie(rep(1,12), col=rainbow(24), radius=0.9, labels=

Dear R developpers, I am a user of Splus since many years and I have developped lots of functions to plot graph of data and model results of irregular or regular times series. In Splus regular times series are created using the rts function and irregular time series using the its functions. In both cases, times is given as a numeric vector. There is no problem in plotting a regular and an

Dear R commnuity, Thanks a lot for your help. I want to read in tables, the problem is that the table is composed in a difficult way. In ariginal it looks like this: 669 736 842101610481029114711811166124312081128117611221026 9581024 992 685 720 829 925 995 96010241057116611501104106410711092 983 908 989 904 924 896 882 897 909 933 928 907 916 902 546 734 784 868 970 954

Synopsis Applications can mmap sound driver DMA memory into their address space (see http://www.4front-tech.com for API documentation). When the application closes the audio fd, it still has the mapping to the DMA buffer. It can now interfere with other apps playing/recording audio. (i.e. the driver should prevent opening the sound driver again while another app holds mappings open to it) But

When running it fails with the following errors from wine: fixme:reg:GetNativeSystemInfo (0x64615408) using GetSystemInfo() fixme:reg:GetNativeSystemInfo (0x64615408) using GetSystemInfo() wine: Call from 0x7b82f500 to unimplemented function ntoskrnl.exe.MmMapIoSpace, aborting wine: Unimplemented function ntoskrnl.exe.MmMapIoSpace called at address 0x7b82f500 (thread 0013), starting debugger...

Dear R-list, I would like to show the implications of estimating a linear trend to time series, which contain significant serial correlation. I want to demonstrate this, comparing lm() and an gls() fits, using the LakeHuron data set, available in R. Now in my particular case I would like to draw confidence bands on the plot and show that there are differences. Unfortunately, I do not know how to

Dear Isabelle, for most of my work I have switched from S-Plus to R, because it is easier to make scripts with due to the better documentation and some other more reasonable implementations. And there was also a cost issue with the S-Plus Server, which is not for free at all. In R I use the its package for time series, which is quite sufficient for my needs. The only problem I encountered was

Many of the security problems we''ve had to deal with recently have been where one program invokes another when no good level of trust exists between them. Currently solutions to this requirement are produced on an ad-hoc basis, usually involving set-id programs (often setuid root), superuser daemons calling user programs for `upcalls'', or both. There are problems with this

Hi I tried to customize help(), so it starts the compiled html help, and searches in all packages. I wrote the following function: > my.help function(topic){ help(topic, package=.packages(all=T), lib.loc=.lib.loc, chmhelp=T) } When I use it with >my.help(help) give the following error: Error in help(topic, package = .packages(all = T), lib.loc = .lib.loc, : No documentation

Dear R-help members. Maybe this is not the right platform to ask this, but I'm looking desperately for a test which is calculating confidence intervals from diversity measurements (non-normaly distributed) (fishers alpha diversity). I was checking the package "vegan" but there seems to be nothing useful. Does anyone of you know with what package I easily could calculate such a

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

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 <-

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

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

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