similar to: Proper apply function for matrices embedded in lists

Hi: I'm sure this seems like a rudimentary question, but I am not well versed with R syntax for lists. I have a ragged array from which I've removed records (entire rows) with missing data. The functions I used to remove the missing cases resulted in the generation of an R list class object, that looks something like this; mydata [[1]] [,1] [,2] [,3] [1,] 1 2 3 [2,] 4

Hi: I'm having some difficulty properly subscripting a function to remove complete NA rows from a R array object. Could someone please suggest how best to script this out? Data structure: X is an array with 3 dimensions (replicates, Species, Sites) replicates: 1 to 6 but ragged...not all sites equal ,,Site1 Sp1 Sp2 Sp3... Sp50 1 5 4 3 2 2 3 0 0 1 3 NA NA NA NA 4 NA NA NA NA 5 NA

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

Hi, While there are a few howtos floating around, what is the standard way to snapshot guests? I went through and converted from raw to pre allocated meta data qcow2 images for this purpose. Some howtos suggest to do an xml snapshot file as so; <domainsnapshot> <name>UbuntuServer_10.10-16032011</name> <description>Snapshot of OS install and

Hello, I have posted this problem before, but thought I try to explain it a bit better. I'm using the function plm to create a fixed effects model for panel data, my method is therefor "within" my effect is "twoways". My Data contains unbalanced Panels due to missing Values, but contains 309 observation for 11 variables (incl. response), with no missing Values. These 309

useRs, Here is some R-ready data for my question to follow. Of course this data is small snippet from a much larger dataset that is about a decade long. Q<-read.table(textConnection("2002 3 28 15 77.38815 2002 3 28 30 77.09505 2002 3 28 45 76.80196 2002 3 28 60 76.50887 2002 3 28 75 76.50887 2002 3 28 90 76.50887 2002 3 28 105

Given an x-vector with, say, 3 elements, I would like to compute the following vector of permutated products (1-x1)*(1-x2)*(1-x3) (1-x1)*(1-x2)*x3 (1-x1)*x2*(1-x3) x1*(1-x2)*(1-x3) (1-x1)*x2*x3 x1*(1-x2)*x3 x1*x2*(1-x3) x1*x2*x3 Now, I already have the correctly sorted matrix of permutations! So, the input looks something like: #input x<-c(0.3,0.1,0.2) Nx<-length(x) Ncomb<-2^Nx

Dear useRs, i want to split a matrix having 1116rows and 12 columns. i want to split that matrix into 36 small matrices each having 12 columns and 31 rows. The big matrix should be splitted row wise. which means that the first small matrix should copy values which are in first 31 rows and 12 columns of the big matrix. similarly 2nd small matrix should contain values from 32nd to 63rd row of the

So i have a simple question that doesnt require sample code, not sure if that violates posting rules or not. Is this: [1] "111" "112" "113" "114" "115" "116" "118" "119" "120" "123" "125" "126" [13] "127" "128" "132" "137"

Hi, Try this: #mat1 is the data res<-do.call(cbind,lapply(seq_len(nrow(mat1)),function(i) {new1<-do.call(rbind,lapply(seq_len(nrow(mat1[-i,])),function(j) {x1<-rbind(mat1[i,],mat1[j,]); x2<-(abs(x1[1,1]-x1[2,1])*abs(x1[1,5]-x1[2,5]))+(abs(x1[1,2]-x1[2,2])*abs(x1[1,6]-x1[2,6]))+(abs(x1[1,3]-x1[2,3])*abs(x1[1,7]-x1[2,7]))+(abs(x1[1,4]-x1[2,4])*abs(x1[1,8]-x1[2,8]))}));new1}))

amd64:/usr/src/xen-unstable.hg#hg tip changeset: 6597:a1de77c1486c5f35884e9f578b7f33b6bd75c362 tag: tip user: shand@ubuntu.eng.hq.xensource.com date: Sat Sep 3 03:36:29 2005 summary: Enable ''live'' migration from a tools pov; prior to this the ''live'' flag was Which option in make ARCH=xen menuconfig should i disable to get rid of

Dear all, Does anyone knows why the results of a BIOENV (PRIMER v. 6.1.15) are diferent of the bioenv() + mantel() in vegan? Not the spearman correlation, indeed the pseudo-p value. I know that the approach bioenv() + mantel() is biased. So, how the BIOENV (PRIMER) ends with larger p values (permutated). Acctualy how the permutation test in BIOENV (PRIMER) is conducted. The user guide does not

It's tough to reliably reproduce, but I often get stack imbalance warnings when calling max.col() on non-real/double matrix. The code is conditionally PROTECTing but not incrementing its nprot counter for the eventual UNPROTECT. Pretty sure this would fix (but I haven't tested it): Index: array.c =================================================================== --- array.c (revision

Hello everyone, I need to create and work with some big matrices that actually have somewhat over 2 million columns and 117 rows. To do some calculations on such big matrices R just needs too much memory for my PC (4GB installed). So I need a solution to work with large datasets. I'm trying to use the ff-package but I don't think I really understand the whole functionality of the

Dear ExpRts, I would like to perform a function with two arguments over the rows of two matrices. There are a couple of *applys (including mApply in Hmisc) but I haven't found out how to do it straightforward. Applying to row indices works, but looks like a poor hack to me: sens <- function(test, gold) { if (any(gold==1)) { sum(test[which(gold==1)]/sum(which(gold==1))) } else NA }

I'm having trouble setting up the function call to handle two lists of matrices. Each list has 6 matrices - Each matrix is 20x10. I need to do some basic math on corresponding matrices in each list. Here are some outputs of these lists, etc... # first list > length(qc.pm) [1] 6 > dim(qc.pm[[1]]) [1] 20 10 > qc.pm[[1]][1:4,1:4] 441-JP071707.CEL 442-JP071707.CEL

Hi, I am performing Cox proportional hazards regression on a microarray dataset with 15000 genes. The p values generated from the Cox regression (based on normal distribution of large sample theory) showed only 2 genes have a p value less than 0.05. However, when I did a permutation on the dataset to obtained permutated p values, and it turned out about 750 genes had a permutated p value less than

I always bump into a few (very minor) problems when building model matrices with e.g.: train = model.matrix(label~., read.csv('train.csv')) target = model.matrix(label~., read.csv('target.csv')) (1) The two may have different factor levels, yielding different matrices. I usually first rbind the data frames together to "meld" the factors, and then split them apart and

Hello, I have researched a few postings where users mentioned being able to install Asterisk on Mac OS X Panther by adding some code after line 165 in the Makefile and then compiling. This has been unsuccessful for me. I downloaded the asterisk-0.9.0.tar.gz tarball and am trying to install from it. The output I get upon trying to make after editing the Makefile can be viewed at:

Suppose, I have a file "data1" containing: 450 390 467 654 30 542 334 432 421 357 497 493 550 549 467 575 578 342 446 547 534 495 979 479 I can read this file with: scan("data1") Read 24 items [1] 450 390 467 654 30 542 334 432 421 357 497 493 550 549 467 575 578 342 446 [20] 547 534 495 979 479