similar to: "Fast" correlation algorithm

Dear R list, I think my question maybe easy for you but I really spent entire day to resolve it. Say I have a matrix, rows are 6000 genes, columns(1-6) are 3 genotypes (a,b,c) with 2 repeat. I have to use two groups each time for t-test, a vs. c or b vs. c, but I dont know how to write correct codes. Below is my codes, the last two lines are needed to be corrected....

Hi all, I have some microarray data on 40 samples that fall into two groups. I have a value for 480k probes for each of those samples. I performed a t test (rowttests) on each row(giving the indices of the columns for each group) then used p.adjust() to adjust the pvalues for the number of tests performed. I then selected only the probes with adj-p.value<=0.05. I end up with roughly 2000

Hi, I'm working with a big square matrix (15k x 15k) and I have some trouble. I want to delete selecting rows and columns. I'm using something like this: > sel_r=c(15,34,384,985,4302,6213) > sel_c=c(3,151,324,3384,7985,14302) > matrix=matrix[-sel_r,-sel_c] but it works very slow. Does anybody know how to make it in faster way? Thank's -- View this message in context:

I'm working with a genomic data-set with ~31k end-points and have performed an F-test across 5 groups for each end-point. The QA measurments on the individual micro-arrays all look good. One of the first things I do in my work-flow is take a look at the p-valued distribution. it is my understanding that, if the findings are due to chance alone, the p-value distribution should be uniform. In

Hello, I have a question regarding how to speed up the t.test on large dataset. For example, I have a table "tab" which looks like: a b c d e f g h.... 1 2 3 4 5 ... 100000 dim(tab) is 100000 x 100 I need to do the t.test for each row on the two subsets of columns, ie to compare a b d group against e f g group at each row. subset 1: a b d 1 2 3 4 5 ... 100000

Hi dear R-users I try to reproduce the steps included in a LDA. Concerning the eigenvectors there is a difference to SPSS. In my textbook (Bortz) it says, that the matrix with the eigenvectors V usually are not normalized to the length of 1, but in the way that the following holds (SPSS does the same thing): t(Vstar)%*%Derror%*%Vstar = I where Vstar are the normalized eigenvectors. Derror

Hi all, I need to minimize following function : dat <- matrix(rnorm(20000), ncol=2) targetFn <- function(x) { dat <- as.matrix(dat) dat1 <- 1*dat[,1] - (x^2)*dat[,2] return(sd(dat1)) } i.e. I want ro find for which "x" the value of "targetFn" will be minimum, depending on current dataset "dat". Is there any

Hi All, (I have a degree in math, but I am too embarassed to ask my colleagues, so here goes:) I would like to get a vector normal (orthogonal) to a plane formed by two other vectors. In matlab I do this: v1 = [.4, .6, .8]; v2 = [.9, .7, .2]; nn = cross(v1,v2) (gives ~[-.48, .65, -.24] if I do R> cross(v1, v2), I get .94. Huh? Thanks for all your help, again. W

Hi, I am not sure if this is a bug and I apologize if it is something I didn't read carefully in the R extension manual. My initial search on the R help and R devel list archive didn't find useful information. I am using .Call (as written in the R extension manual) for the C code and have found that the .Call didn't release the memory claimed by allocVector. Even after applying

Hi, I am not sure if this is a bug and I apologize if it is something I didn't read carefully in the R extension manual. My initial search on the R help and R devel list archive didn't find useful information. I am using .Call (as written in the R extension manual) for the C code and have found that the .Call didn't release the memory claimed by allocVector. Even after applying

you could try mapply mydata2<-mapply("[", mydata, lapply(mydata, function(x) !x %in% A)) mydata2[[1]]<-A #to replace the obviously deleted elements of "A" mydata2 mydata2[[1]] mydata2[[2]] mydata2[[3]] mydata2[[4]] Stefano -----Messaggio originale----- Da: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch]Per conto di jastar

We have a problem inverting a matrix which has the following eigenvalues: > eigen(tcross, only.values=TRUE) $values [1] 7.917775e+20 2.130980e+16 7.961620e+13 8.241041e+12 2.258325e+12 [6] 3.869428e+11 6.791041e+10 2.485352e+09 9.863098e+08 9.819373e+05 [11] 3.263408e+05 2.929853e+05 2.920419e+05 2.714355e+05 8.733435e+04 [16] 8.127136e+04 6.543883e+04 5.335074e+04

I have a data frame with a number of observed and predicted values by classification as shown below: Count Volume FCLASS 1 55000 60000 Grade Separated 2 43000 39000 Grade Separated 3 26000 26500 Major Arterial 4 19500 20000 Major Arterial ... There are four classes here: Grade Separated, Major Arterial, Minor Arterial, and Collector I am looking

Dear All, I have a problem that I think could be solved much more efficiently, but I don't have a clue how to accomplish this. I have a matrix W with dimensions k*(p+1): Let's say W is 5*4 and looks like this: > W [,1] [,2] [,3] [,4] [1,] 1 -1 -1 1 [2,] 1 1 1 1 [3,] 1 2 -2 -1 [4,] 1 0 -1 -1 [5,] 1 -2 -1 0 I want to take

Hi, I have a problem. Please, look at example and try to help me!! > A<-c("aaa","bbb","ccc","ddd","eee") > B<-c("vvv","ooo","aaa","eee","zzz","bbb") > C<-c("sss","jjj","ppp","ddd") > D<-c("bbb","ccc")

Dear R gurus, I have a very embarrassingly parallelizable job that I am trying to speed up with snow on our local cluster. Basically, I am doing ~50,000 t.test for a series of micro-array experiments, one gene at a time. Thus, I can easily spread the load across multiple processors and nodes. So, I have a master list object that tells me what rows to pick up for each genes to do the t.test from

Hi guys, I am still learning R, and not well familiar with all the apply functions. I am trying to find faster alternatives to replace the for cycle. Is there a faster way to do the example below? nm <- 1000 b <- matrix (rnorm (5000, 0, 1), nrow = 500, ncol = nm) a <- matrix (0, nm, nm) for (i in 1 : nm) { for (j in 1 : nm) { if ( j == i) { next } a[i, j] <- t (b [, i]) %*% b[, j] } }

Dear R-experts! I have y(x) data, dim(y) == dim(x) == c(2000, 2) I'd like to fit them with nls: fit.result <- nls ( y ~ f(x, p1, p2, p3), start = list(p1 = ... , p2 = .. , p3 = ..) ) Actually I want to fit y[,1] ~ x[,1] and y[,2] ~ x[,2] *simulaneously*, with the same parameters set {p1, p2, p3}. I tried to feed R tha above formula, R errors with:

with samba 3.0.22, I'm trying to integrate a linux box with Microsoft AD by using winbind for authentication as well as for the source of nss info. When winbind is configured to use its own local id maps, everything works fine. But when i configure winbind to use 'ad' as the source of nss info, authentication fails, 'getent' commands return no results, and 'wbinfo -r

Dear All, I have written the following programs to find a non-singular (10*10) covariance matrix. Here is the program: nitems <- 10 x <- array(rnorm(5*nitems,3,3), c(5,nitems)) sigma <- t(x)%*%x inverse <- try(solve(sigma), TRUE) while(inherits(inverse, "try-error")) { x <- array(rnorm(5*nitems,3,3), c(5,nitems)) sigma <- t(x)%*%x inverse <-