similar to: Significance analysis of Microarrays (SAM)

Hi, just during these vacation days, I'm trying to approach with multicore package and I have some troubles with foreach. What I'm trying to do is to extract a data in coordinate (ii,jj) from a matrix2, only if the data in the same coordinate in matrix1 is ==1. Make this with a nested "for" take a lot of time because I have thousand of values. ex. Binary_hex = NULL

Hi, I have two table matrix, and I would like to compare the different between two matrix. For example: Matrix 1: A B C A 0 1 0 B 0 0 1 C 0 0 0 Matrix 2: A B C A 0 1 0 B 0 0 0 C 0 0 0 Each column which have value 1, should also return value 1. As in this case/example, the result should appear like this (as below). The result of this differentiation should also be in matrix

Dear All: Significance Analysis of Microarrays(SAM) As we know sam do multiple t.test as following ## Default S3 method: t.test(x, y = NULL, alternative = c("two.sided", "less", "greater"),mu = 0, paired = FALSE, var.equal = FALSE,conf.level = 0.95, ...) var.equal: a logical variable indicating whether to treat the two variances as being equal. If 'TRUE'

Dear R team, The following piece of code (to use the lsa package) works fine on my mac os x, but when I run the same code on Windows XP, it doesn't work any more. ### code: library("lsa") matrix1 = textmatrix("C:\\Documents and Settings\\tine stalmans.TINE. 000\\LSA\\cuentos\\", stemming=TRUE, language="spanish", minWordLength=2, minDocFreq=1,

I have two matrix with same dimensions. I want to do t.test using each column from 2 different matrix. Row n Column names in both matrix are same. e.g. Matrix1 id VC1 VC2 VC3 R 1 2 3 R1 4 5 6 R3 7 8 9 Matrix2 id VC1 VC2 VC3 R 10 11 12 R1 13 14 15 R3 16 17 18 want to do t.test using each column (with same name ) using Matrix1 and Matrix2 for eg

I have a question related to matrix(). The code below randomly generates 3 Poisson numbers into a 3 by 1 matrix: > matrix1 <- matrix(rpois(3,lambda=2),nrow=3,ncol=1) And I use list() to see what they are: [,1] [1,] 3 [2,] 1 [3,] 4 , which is what I had intended. I then I want to randomly generate y Normal numbers into a 3 by 8 matrix. y in this case would be 3, 1, and 4; so

I'm performing item response theory with eRm packages I am excluding the persons that doesn't fit in the infit/outfit persons. for that I created a condition. then I have to create a new subset or matrix but with the condition. So: ORIGINAL matrix<-cbind(item1, item2, item3, item4) IF I PERFORM A head(matrix) item1 item2 item3 item4 3 2 3 1 3 1

Hi, On R 2.10.1 for Windows, when I do the following to duplicate the structure of a large numeric matrix called matrix1: matrix2 <- matrix(0,nrow=nrow(matrix1),ncol=ncol(matrix1)) and then rownames(matrix2) <- rownames(matrix1) I get a "cannot allocate vector of size xxMb" error but if I instead do: rnames <- list() rnames <- rownames(matrix1) matrix2 <-

So I have a function that does lapply's for me based on dimension. Currently only works for length(pivotColumns)=2 because I haven't fixed the rbinds. I have two versions. One runs WAYYY faster than the other. And I'm not sure why. Fast Version: fedb.ddplyWrapper2Fast <- function(data, pivotColumns, listNameFunctions, ...){ lapplyFunctionRecurse <- function(cdata, level=1,

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, thank you for taking the time and reading my question. My question is twofold: 1. I have several matrices with variables and one matrix with water levels. I want to predict the water level with the data in the other matrices. Basically, * mod<-lm(matrix1 ~ matrix2+matrix3)* ( What looks like a minus is meant to be the wiggly minus.) Of course I could dissemble the matrices and paste

Hello, I have got two matrices with different length. The matrices have 3 columuns. The first two are coordinates. The third is a measurement. Now I want to get a subtraction between every single value of the third column (between matrix1 and matrix2), but only if the two first coordinates in matrix1 and matrix2 are the same. I tried "FUN=?" in aggregate and ave, but I don't know

Hi, I'm new to programming, new to R and even new to mailing lists so please be patient with me. I need to manage many matrices generated by an R program. These matrices have different dimensions and I'd like to group them somehow. The best way would be to have a big matrix (let's call it database) where every element database[x,y] consists of a list of matrices that all have the

I have a matrix with a lot of values inside.. when I execute the folowing command matrix2=subset(martix, condition.....) it works... but after the previous command I execute another "subset" matrix3=subset(martix2, condition2.....) and appears the following error: (subscript) logical subscript too long How can I solve this??? I think the probles is the size of the matrix...

Dear List, With regard to the question I previously raised, here is the result I obtained right now, brglm() does help, but there are two situations: 1) Classifiers with extremely high AIC (over 200), no perfect separation, coefficients converge. in this case, using brglm() does help! It stabilize the AIC, and the classification power is better. Code and output: (need to install package:

I have two matrix with same dimensions. I want to do t.test using each column from 2 different matrix. Column names in both matrix are same. e.g. Matrix1 id VC1 VC2 VC3 R 1 2 3 R1 4 5 6 R3 7 8 9 Matrix2 id VC1 VC2 VC3 R 10 11 12 R1 13 14 15 R3 16 17 18 want to do t.test for eg t.test(Matrix1$VC1, Matrix2$VC1)$p.value What is the best way to do it. I have dataset with

Hi R-users I have a series of matrices and I would like to bind them together by column using a loop (i.e. not writing cbind(matrix1, matrix2,matrix3....)). The reason is because in my real data set I have a very large number of matrices. Example: matrix1 <- matrix (1:12,4,3) matrix2 <- matrix (13:24,4,3) matrix3 <- matrix (25:36,4,3) matrix4 <- matrix (37:48,4,3)

Dear experts: Is it possible to create a new function based on stats:::model.matrix.default so that an alternative factor coding is used when the function is called instead of the default factor coding? Basically, I'd like to reproduce the results in 'mat' below, without having to explicitly specify my desired factor coding (identity matrices) in the 'contrasts.arg'. dd

Let's say that I have a bunch of matrices. They look like this (pardon using fruit for examples, my actual data tables are far too enormous): Matrix1 Apples Oranges Pears A 5 6 7 B 5 3 4 C 8 9 10 D 11 13 14 E 15 3 8 F 1 4 5

Dear list, I have two martix like these, each rows with name in the matrix. I try to select rows with identical name from two matrix and calculate their correlation. Matrix1: 2-Sep 9 5.5 1 7.18 10.0 8 139.45 2-Sep 7 4.9 15 8.22 61.68 9 123.62 4-Sep 5-Sep AAMP A2M A2M . . . Matrix2: 2-Sep 4 7 1 6.8 12 8 19.4