similar to: Confidence interval from resampling

Dear all, Let say I have following data: RawData <- matrix(1:101, nr = 1); colnames(RawData) <- c("ASD", as.character(as.yearmon(seq(as.Date("2012-03-01"), length.out = 100, by = "1 month")))); rownames(RawData) <- "XYZ" CutOffDate <- as.Date("2012-09-01") NewDateSeries <- as.character(as.yearmon(seq(CutOffDate, to =

I'm trying to generate some plots for print publication and I'm having trouble getting them the way I want. I get a plot with a legend that overlaps the bars and looks bad. Also, I'm saving this as PDF file (whatever happened to the "png" function to save as PNG format, in r 1.4.1?) I've tried a number of things, which produce a number of questions. 1) it seems that the

Hi everyone, I'm having trouble applying the Cor() function to two matrices, both of which contain NAs. I am doing the following: a<-cor(m1, m2, use="complete.obs") ... and I get the following error message: Error in cor(m1, m2, use = "complete.obs") : no complete element pairs Does anyone know how I can apply a correlation, ignoring any NAs? Thanks, rcoder --

Hi, there, I put roaming profiles for NT4 users on samba server. In "Domain User Manager", I set "User profile Path" \\abel\profile\%U.pds, here abel is samba server. It works well, but everyday several users complain. Sometime, they cannot login because of roaming profiles. Sometimes, some users cannot run VC or VB. Sometime, I only need to remove ntuser.dat on samba

Hi all, I need to run a program (asreml) thousands of times and each time I have to provide a slightly different dataset. Because I have to run asreml under Windows (DOS or scripts) environment, I have trouble to pass a command variable (or pointer variable or %counter in the following example) to R program so at each counter R can generate a different data set for asreml to run. Any

Hi all, How can I have a dynamic list for different combinations of grouping factors in the following example? Thanks. dat <- data.frame(x=rnorm(100), a=sample(letters[1:5], replace = T), b=sample(letters[1:5], replace = T), c=sample(letters[1:5], replace = T), d=sample(letters[1:5], replace = T)) ## define grouping

Dear All, I fear I am badly misunderstanding something fundamental about the mclust package. Please considered the dataset pasted at the end of the email (you can also have a look at http://dl.dropbox.com/u/5685598/dataedges.csv ). Now, I would like to use the info on columns 1,2 and 4 to predict the value in column 3. However, when I run the script below (obg_mining_script.R), I get

Hi everyone, I'm experiencing difficulty getting the results I want when I use a nested for loop. I have a data set to which I perform some calculations, and then try to apply a regression over a rolling window. The code runs, but the regression results I am getting (intercept and slope) are simply the same, repeated again and again in the results matrix. The regression does not seem to be

depends on "autoconf: regenerate configure scripts with 4.4 version" This series removes some of the really old deadwood from the tools build and makes some other things which are on their way out configurable at build time with a default depending on how far down the slope I judge them to be. * nuke in tree copy of libaio * nuke obsolete tools: xsview, miniterm, lomount & sv *

Hi all, While dat['a1',] and dat['a10',] produce the same results in the following example, I'd like dat['a1',] to return NAs. dat <- data.frame(x1 = paste(letters[1:5],10, sep=''), x2=rnorm(5)) rownames(dat) <- dat$x1 dat['a1',] dat['a10',] > sessionInfo() R version 2.7.2 (2008-08-25) i386-pc-mingw32 locale: