similar to: Help with a repeating process

Hey all, I am trying to generate a number of vectors with string combinations. The below code solves my initial problem, however I would prefer not hardcoding the positions of the 'objects' as in the future the number of 'objects' could increase. Is there a way to loop over these objects and generate the same output? The same goes for the 'maxspacer'. Thanks a whole

Hello Andreas I?ve read yor thread about the opening word files read only protection failure. I have the same problem. Have you got any answer on the problem you will share. Best regards Michael ---------------------------------------- Michael B?rjesson Systemtekniker/N?tverkstekniker Wettergrens Kontorscenter AB Argongatan 32 431 51 M?lndal tel: 031-706 25 00 mobil: 031-760 73 508

Dear R developers, I am using R under Linux, but I would like to share an extension package with some Windows users. The package contains only data and .R scripts. There is no src directory. So, I think I do not need a Windows machine with C compiler, "make", "sh" and "perl". If I am wrong, please tell me. I tried the following approaches (after verifying the

Full_Name: Petr Savicky Version: 2.4.0 OS: Fedora Core release 2 Submission from: (NULL) (62.24.91.47) the error is > binom.test(0.56*10000,10000) Error in binom.test(0.56 * 10000, 10000) : 'x' must be nonnegative and integer while > binom.test(5600,10000) yields correct result. The same error occurrs for > binom.test(0.57*10000,10000)

In R-2.10.0, the development version, function as.factor() uses 17 digit precision for conversion of numeric values to character type. This is very good for the consistency of the resulting factor, however, i expect that people will complain about, for example, as.factor(0.3) being [1] 0.29999999999999999 Levels: 0.29999999999999999 I suggest to extend the "Warning" section of

Dear All, I have two matrices A (40 x 732) and B (40 x 1230) and would like to calculate correlation between them.  I can use: cor(A,B, method="pearson") to calculate correlation between all possible pairs. But the issue is that there is one-many specific mappings between A and B and I just need to calculate correlations for those pairs (not all). Some variables in A (proteins, say p1)

Full_Name: Petr Savicky Version: 1.5.0 OS: SuSE 7.2 Submission from: (NULL) (147.231.6.1) The function tree() from the tree package crashes for certain input data. All the information needed to reproduce the error (data, script, expected error message) may be found at http://www.cs.cas.cz/~savicky/tree_error/tree_error.tgz Exactly the same error appeared at another computer running some RedHat

Dear List, I will appreciate any advice regarding how to convert the following numbers [I got in return by taxondive()] in numeric integers without the e.g. 6.4836e+01 abbreviations. Thank you very much in advance, Gian > taxa_dive Species Delta Delta* Lambda+ Delta+ S Delta+ Nat1 5.0000e+00 6.4836e+01 9.5412e+01 6.7753e+02 8.7398e+01 436.99 Nat2

Hello All, My algorithm as follows; y <- c(1,1,1,0,0,1,0,1,0,0) x <- c(1,0,0,1,1,0,0,1,1,0) n <- length(x) t <- matrix(cbind(y,x), ncol=2) z = x+y for(j in 1:length(x)) { out <- vector("list", ) for(i in 1:10) { t.s <- t[sample(n,n,replace=T),] y.s <- t.s[,1] x.s <- t.s[,2] z.s <- y.s+x.s out[[i]] <- list(ff <- (z.s), finding=any (y.s==y[j])) kk

List members, Via searches I've seen similar discussion of this topic but have not seen resolution of the particular issue I am experiencing. If my search on this topic failed, I apologize for the redundancy. I am attempting to generate random covariance matrices but would like the corresponding correlations to be uniformly distributed between -1 and 1. The approach I have been using is:

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,

Dear all, I need to generate numbers from multivariate normal with large dimensions (5,000,000). Below is my code and the error I got from R. Sigma in the code is the covariance matrix. Can anyone give some idea on how to take care of this error. Thank you. Hannah > m <- 5000000 > m1 <- 0.5*m > rho <- 0.5 > Sigma <- rho* matrix(1, m, m)+diag(1-rho, m)

Dear all, I have question but cannot explain without providing some context first: I want to calculate how many policy-connected coalitions between 7 parties are possible. I have positions on an one-dimensional scale for each party and I have sorted the parties on the positions (it is sorted from extreme left to extreme right, hence using a left-right scale). A policy-connected coalition

Dear list, I have a big deal concerning the development of a Taylor expansion. require(Matrix) e1 <- as.vector(1:5) e2 <- as.vector(6:10) in order to obtain all the combinations between these two vectors following a Taylor expansion (or more simply through a Maclaurin series) for real numbers. We have f(x) = f(0) + f'(0)(x-0) + f''(0)(x-0)^2/2! + ? + f^(k)(0)(x-0)^k/k!

I have a C function, which performs a transformation of a large integer matrix. The matrix may be of size 1.6GB, so I can have only one copy in RAM and have to modify it in place. This is possible using .Call and works fine. For debugging, I need two copies of a smaller matrix and modify only one of them. This may also be done, for example, by A <- some integer matrix B <- A +

lgamma(x) and lfactorial(x) are defined to return ln|Gamma(x)| {= log(abs(gamma(x)))} or ln|Gamma(x+1)| respectively. Unfortunately, we haven't chosen the analogous definition for lchoose(). So, currently > lchoose(1/2, 1:10) [1] -0.6931472 -2.0794415 NaN -3.2425924 NaN -3.8869494 [7] NaN -4.3357508 NaN -4.6805913 Warning message: In

Hello, I am stuck with selecting the right rows from a data frame. I think the problem is rather how to select them then how to implement the R code. Consider the following data frame: df <- data.frame(ID = c(1,2,3,4,5,6,7,8,9,10), value = c(34,12,23,25,34,42,48,29,30,27)) What I want to achieve is to select 7 rows (values) so that the mean value of those rows are closest to the value of 35

Hello Members, I need to group a data.frame in a a specific way, like shown below, DF raw is like this, id col1 col2 score 1 A B 40 2 B C 55 3 C D 4000 4 D E 100 5 E F 300 I want the out put as List [1] A B C [2] D E [3] F Basically the split should be based on the DF$score > 200, and all the col1 and col2 values should be

Dear all, I am using the ROCR library to compute the AUC and also the Hmisc library to compute the C-index of a predictor and a group variable. The results of AUC and C-index are similar and give a value of about 0.57. The Wilcoxon p-value is <0.001! Why the AUC is showing small value and the p-value is high significant? The AUC is based on Wilcoxon calculation? Many thanks, Lina

I'm running Samba on my Sun ultra 1 with Solaris 2.6 and have noticed that my log.nmb file is growing tremendously. In addition, my log.smb is also growing but not quite as fast. The log.nmb has the following entries repeating every few seconds. Since 3/18/99, my log.nmb file has grown to be 22mb. [1999/06/14 09:13:14, 1] nmbd/nmbd.c:(672) Netbios nameserver version 2.0.0 started.