similar to: array addition

Dear R listers, I am trying to create a new variable that uniquely identifies groups of observations in a dataset. So far I couldn't figure out how to do this in R. In Stata I would simply type: egen newvar = group(dim1, dim2, dim3) Please, find below a quick example to show what I am dealing with: I have a dataset with 4 variables: var <- runif(50) ## a variable that I want to group

Below is code that introduces a list comprehension syntax into R, allowing expressions like: > .[ sin(x) ~ x <- (0:11)/11 ] [1] 0.00000000 0.09078392 0.18081808 0.26935891 0.35567516 0.43905397 [7] 0.51880673 0.59427479 0.66483486 0.72990422 0.78894546 0.84147098 > .[ .[x*y ~ x <- 0:3] ~ y <- 0:4] [,1] [,2] [,3] [,4] [,5] [1,] 0 0 0 0 0 [2,] 0 1 2

Full_Name: Manuel L?pez-Ib??ez Version: R version 2.6.2 (2008-02-08) OS: linux-gnu Submission from: (NULL) (164.15.10.156) This is only relevant for CPU intensive child processes. Otherwise, the problem is not obvious. Therefore, we need a CPU intensive program like this one: /************************************/ /*** Compile with: gcc -o timer-test -O0 timer-test.c -lm */ #include

Hi, I would like to use the multinomRob function to test election results. However, depending on which independent variables I include and how many categories I have in the dependent variable, the model cannot be estimated. My data look like this (there are 68 observations): > head(database) RESTE09 GAUCHE09 PDC09 PLR09 UDC09 MCG09 RESTE05 GAUCHE05 PDC05 D1 1455

Dear all, I had to extracts/set elements from/in a matrix. Let say I have two vectors dim1 and dim2 of indices in the respective two dimensions of a matrix: I want to extract all the corresponding elements. I the case of a nxn matrix, dim1 <- 1:n and dim2 <- 1:n would extract the diagonal. I know one way would be to use the functions 'row' and 'col', but the matrixes I

foo <- rnorm(30*34*12) dim(foo) <- c(30, 34, 12) I want to make a data.frame out of this three-dimensional array. Each dimension will be a variabel (column) in the data.frame. I know how this can be done in a very slow way using for loops, like this: x <- rep(seq(from = 1, to = 30), 34) y <- as.vector(sapply(1:34, function(x) {rep(x, 30)})) month <- as.vector(sapply(1:12,

The latter is a better way to disable the qemu display output as we need to, without enabling extra devices (which are disabled already, anyway). Also, related to the change above, ban the -display parameter from the ones that can be supplied by the user. --- configure.ac | 8 ++++---- src/launch-direct.c | 12 ++++++++---- src/launch.c | 1 + 3 files changed, 13 insertions(+), 8

Dear R-help community, I am working with multidimensional contingency tables and I am having trouble getting loglm to run on all dimensions without typing out each dimension. I have generated random data and provided output for the results I want below: d1.c1 <- rnorm(20, .10, .02) d1.c2 <- rnorm(20, .10, .02) d2.c1 <- rnorm(20, .09, .02) d2.c2 <- rnorm(20, .09, .02) d3.c1 <-

Hello all, I have been using the function ordirgl to plot 3D dynamic ordinations. The ordirgl function works just fine. IN fact, I was even able to write a function that allows me to identify points in the 3D plot: identify.rgl<-function(env_var,ord,dim1,dim2,dim3) { tmp<-select3d(button="left") tmp.keep<-tmp(ord[,dim1],ord[,dim2],ord[,dim3])

v1 -> v2: - Rebase on top of Pino's version work. Two patches went upstream, these are the two remaining patches. Note the generation number is still inside the qemu.stat file. We could put it in the filename, I have no particular preference. Rich.

Hello R experts, I've used metaMDS to run NMDS on some fish abundance data, and am also working on correlating environmental data to the NMDS coordinates. I'm fairly new to metaMDS and NMDS in general, so I have what are probably some very basic questions. My fish abundance data consists of 66 sites for which up to 20 species of fish were identified and counted. I ran metaMDS on this data

Dear R users, I'am using marray and Limma packages to analyze genepix output. 1) how can I filter bad spots from my data (data contains 3 types of bad spots). my experiment contains 12 samples and the bad spot are not associated to the same probes 2) how can I remove control probes from my data ? I'm sorry, i'm new with R and I can't find answer in packages doc. best regards,

Buenos días -- Francisco Maturana Miranda Dr. Planificación territorial, urbanismo y dinámicas del espacio Profesor Asociado, Departamento de Geografía Universidad Alberto Hurtado www.fmaturana.cl [[alternative HTML version deleted]]

Hello I have 2 columns of short sequences that I would like to compare and count the number of mismatches and record the number of mismatches in a new column. The sequences are part of a data frame that looks like this: seq1=c("CGGTGTAGAGGAAAAAAAGGAAACAGGAGTTC","CGGTGGTCAGTCTGGGACCTGGGCAGCAGGCT", "CGGGCCTCTCGGCCTGCAGCCCCCAACAGCCA")

Hi I am wanting to time how long it takes a couple commands to run. I can do "time command" and it tells me. How do I do multple commands at a time. I tried: time command; command no good time "command; command" no good I can make a script I guess but thought there was a more elegant way. Thanks, jerry

I would like to create a subtotal table with custom bands. seq1 = seq(0, 100, by = 5) seq2 = seq(100, 1000, by = 100) Bands = c(seq1, seq2) #Prices Prices = sample(1:1000, 200, replace=F) #corresponding size for the given price above. size = sample(1:1000, 200, replace=F) How would I find the subtotal of the size based on a given price falls within a band? -- View this message in

Dear list: I have tried MASS's mca function and SAS's PROC corresp on the farms data (included in MASS, also used as mca's example), the results are different: R: mca(farms)$rs: 1 2 1 0.059296637 0.0455871427 2 0.043077902 -0.0354728795 3 0.059834286 0.0730485572 4 0.059834286 0.0730485572 5 0.012900181 -0.0503121890 6

Doing qemu feature detection in the direct backend takes ~100ms because we need to run `qemu -help' and `qemu -devices ?', and each of those interacts with glibc's very slow link loader. Fixing the link loader is really hard. Instead memoize the output of those two commands. This patch series first separates all the code dealing with qemu into a separate module (src/qemu.c) and

I am using exim via dovecot_deliver to store messages in Maildir in my $HOME. I am using kmail to retrieve stuff. Unfortunately, something in my data crashes dovecot. I was using 1.0.rc14 from opensuse, but downloaded and installed 1.0.8 from the site. Here is the crash: Dec 5 18:05:09 h743107 dovecot: IMAP(kris): file mail-index-transaction.c: line 629 (mail_index_update_flags_range):

The patch I posted last week to disable mandatory locking for readonly drives (https://www.redhat.com/archives/libguestfs/2017-September/msg00013.html) was wrong in a couple of respects. Firstly it didn't work, which I didn't detect because my tests were testing the wrong thing. Oops. Secondly it used a simple version number check to detect qemu binaries implementing mandatory locking.