similar to: Combine Values into a Vector or List

Dear mailing list members, I am a new R user, I would like to plot the follewing data var1 <- c(1,2,1,1,2,1,2,1,2,2) var2 <- round(rgamma(10,2,1)/0.1)*0.1 var3 <- c(0,1,0,1,0,0,0,0,1,0) var4 <- c(1,2,2,2,1,1,1,1,1,1) Var <- data.frame(var1,var2,var3,var4) Var <- Var[sort(Var$var1),] tt <- Var$var1+((runif(length(Var$var1))/6)-(0.5/6)) labelname <- c("time 1",

Hi, You might need to check library(boot).? I have never used that before.? So, I can't comment much.? It is better to post on R-help list.? I had seen your postings on Nabble in the past.? Unfortunately those postings were not accepted in R-help.? You have to directly post at ? r-help at r-project.org after registering at: https://stat.ethz.ch/mailman/listinfo/r-help ?

As promised, here's a re-send of all my Aarch64 patches, following comments by John Ridges. Note that they actually affect more than just Aarch64 -- other than the ones specifically guarded by AARCH64_NEON defines, the Neon intrinsics all also apply on armv7; and the OPUS_FAST_INT64 patches apply on any 64-bit machine. The patches should largely be independent and independently useful, other

Here are my aarch64 patches rebased to the current tip of Opus master. They're largely the same as my previous patch set, with the addition of the final one (the Neon fixed-point implementation of xcorr_kernel). This replaces Viswanath's Neon fixed-point celt_pitch_xcorr, since xcorr_kernel is used in celt_fir and celt_iir as well. These have been tested for correctness under qemu

Following Tim's comments, here are my reworked patches for the Neon intrinsic function patches of of my Aarch64 patchset, i.e. replacing patches 5-8 of the v2 series. Patches 1-4 and 9-18 of the old series still apply unmodified. The one new (as opposed to changed) patch is the first one in this series, to add named constants for the ARM architecture variants. There are also some minor code

Full_Name: Richard Cotton Version: 2.6.1 OS: Windows XP (32bit) Submission from: (NULL) (193.119.236.82) Using %s in a filename when opening a device causes R to crash, e.g., pdf("foo%s.pdf") win.metafile("foo%s.wmf") postscript("foo%s.ps")

I've just found some behaviour which strikes me as odd, but I'm not sure whether it's a bug or a feature. If you don't mind, I'd like to explain via a couple of examples. Let x = 1:10. Then intuitively, to me at least, the command x[-integer(0)] should leave x untouched. However the actual output under R2.4.0 is integer(0). A slightly more involved example demonstrates

Hi, Can we get the code for calculating Oja median for multivariate data Thanks and Regards Rahul Agarwal Analyst Equities Quantitative Research UBS_ISC, Hyderabad On Net: 19 533 6363 [[alternative HTML version deleted]]

I want to run deconvolution of a time series by an impulse or point-spread function through Wiener filter, regularized filter, Lucy-Richardson method, or any other approaches. I searched the CRAN website and the mailing list archive, but could not find any package for such a deconvolution analysis. Does anybody know an existing R function for deconvolution? TIA, Gang

Hi, Using S=1000 and simdata <- replicate(S, generate(3000)) #If you want both "m1" and "m0" #here the missing values are 0 res1<-sapply(seq_len(ncol(simdata.psm1)),function(i) {x1<-merge(simdata.psm0[,i],simdata.psm1[,i],all=TRUE); x1[is.na(x1)]<-0; x1}) res1[,997:1000] #????? [,1]???????? [,2]???????? [,3]???????? [,4]??????? #x1??? Numeric,3000 Numeric,3000

Hi, I have a data frame with 6563 observations. I can run a regression with loess using four explanatory variables. If I add a fifth, R crashes. There are no missings in the data, and if I run a regression with any four of the five explanatory variables, it works. Its only when I go from four to five that it crashes. This leads me to believe that it is not an obvious problem with the data,

Hopefully an easy question. When drawing a rectangles in a lattice plot key, how do you omit the black borders? Here is an example adapted from one on the xyplot help page: bar.cols <- c("red", "blue") key.list <- list( space="top", rectangles=list(col=bar.cols), text=list(c("foo", "bar")) ) barchart( yield ~ variety | site,

When data contains both factor and numeric variables, how to get quartiles for all numeric variables? n <- 100 x1 <- runif(n) x2 <- runif(n) x3 <- x1 + x2 + runif(n)/10 x4 <- x1 + x2 + x3 + runif(n)/10 x5 <- factor(sample(c('a','b','c'),n,replace=TRUE)) x6 <- factor(1*(x5=='a' | x5=='c')) data1 <- cbind(x1,x2,x3,x4,x5,x6) data

Dear all, I'm analysing a survey, and creating a barchart of the different responses for each question. The questions are grouped according to a number of categories, so I'm using lattice to create a plot with each question in a category on it. The problem is that the response set for different questions within the same category varies. I want to be able to draw only the relevant

Apologies if this isn't acceptable for the general help list. I'm running OpenBUGS model via the R2WinBUGS package interface, under Windows. Is it possible to terminate running models, short of using the Windows Task Manager to forcibly exit the program? Regards, Richie. Mathematical Sciences Unit HSL ------------------------------------------------------------------------

Hi, I have to match names where names can be recorded with errors or additions. Now I am searching for a string search function which returns always the "closest" match. E.g. searching for "Washington" it should return only Washington but not Washington, D.C. But it also could be that the list contains only "Hamburg" but the record I am searching for is

I'm trying to set up a lattice plot with two y-axes for each panel. (Yes, I know that multiple y-axes are generally a bad idea; the graph is for someone else and they want it that way.) I've used a custom yscale.component in xyplot to achieve this: myyscale.component <- function(...) { ans <- yscale.components.default(...) ans$right <- ans$left foo <-

Dear All, I am new to R, I have one question which might be easy. I have a large data with more than 250 variable, i am reducing number of variables by redun function as in the example below, n <- 100 x1 <- runif(n) x2 <- runif(n) x3 <- x1 + x2 + runif(n)/10 x4 <- x1 + x2 + x3 + runif(n)/10 x5 <- factor(sample(c('a','b','c'),n,replace=TRUE)) x6 <-

Hello, I have a survivor curve that shows account cancellations during the past 3.5 months.  Fortunately for our business, but unfortunately for my analysis, the survivor curve doesn't yet pass through 50%.  Is there a safe way to extend the survivor curve and estimate at what time we'll hit 50%? We started a new program 3.5 months ago, and I believe that this set of accounts behaves

I have a large data frame ("data1") that looks like: A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 [1,] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 [2,] 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 0 0 [3,] 1 1 0 1 1 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 [4,] 1 1 1 1