similar to: inverse of matrix made by low.tri function

Dear all, I need to create a for-loop in which I can compute multiple histograms My code is the following : #singlefile includes huge csv file #I want to specify the binsize #I would like to compute in the for -loop the histograms numfiles <- length(singlefile) for (i in 1 :51) { binsize <- -20 :20/2 hist(singlefile(singlefile$GVC[singlefile$new_id==i]], break=seq(),

Dear R community, I am new to R, a reforming SAS user :) I am running R 2.10.1 on a Windows XP machine. I would like to write linear functions of my coefficient parameter estimates from a glm, but am having a difficult time understanding the parameterization R uses. In the toy example below I am running a glm on binomial data, with clones and lines within clones as fixed effects, each with 6

My data looks like: A,B,C,D,Class 1,2,0,2,cl1 1,5,1,9,cl1 3,2,1,2,cl2 7,2,1,2,cl2 2,2,1,2,cl2 1,2,1,5,cl2 0,2,1,2,cl2 4,2,1,2,cl2 3,5,1,2,cl2 3,2,12,3,cl2 3,2,4,2,cl2 **The steps followed are: trainfile <- read.csv("TrainFile",head=TRUE) datatrain <- subset(trainfile,select=c(-Class)) classtrain <- (subset(trainfile,select=Class)) rf <- randomForest(datatrain, classtrain)

List, In order to get rid of some old, unreadable S3 code in package sp, I'm trying to rewrite things using S4 methods. Somewhere I fail, and I cannot sort out why. In order to isolate the problem, I created two functions, doNothing<- and dosth, and both should do nothing. The issue is that in most cases they do nothing, but in some cases dosth(obj) changes the class of obj and breaks with

Hi, I have generated a skyline plot of a tree in newick format using "ape". How can I plot the error bars for this graph? I only have the the tree data. un<-"((((8.1:0, 20.1:0):0, 6.1:3):123, (((((35.1:0, (22.1:0, (43.1:1, 29.1:0):0):0):4, 25.1:6):0, ((42.1:0, 21.1:0):3, (39.1:0, 2.1:0):3):1):8, (3.1:0, 7.1:0):8):48, (((((15.1:0, 14.1:0):0, 11.1:0):0, 37.1:0):0,

Here's a skeletal example. Embellish as needed: p <- 5 n <- 300 set.seed(1) dat <- cbind(rnorm(n), matrix(runif(n * p), n, p)) write.table(dat, file="c:/temp/big.txt", row=FALSE, col=FALSE) xtx <- matrix(0, p + 1, p + 1) xty <- numeric(p + 1) f <- file("c:/temp/big.txt", open="r") for (i in 1:3) { x <- matrix(scan(f, nlines=100), 100,

Dear All, I've had trouble compiling DPpackage as a user in one system. It works fine as root in other machines. I can see any clues in error messages My guess is that it is a permissions matter. Any help is appreciated. OS: Linux Kernel: 2.6.27 SMP Arch: Intel 64 bits gfortran not available Thank you. ----------------------><8------------------------------------- g77 ? -fpic ?-g

i am trying to use matinv from the Design package to compute the generalized inverse of the normal equations of a 3x3 design via the sweep operator. That is, for the linear model y = ? + x1 + x2 + x1*x2 where x1, x2 are 3-level factors and dummy coding is being used the matrix to be inverted is X'X = 9 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 3 3 0 0 1 1 1 1 0 0 1 0 0 1 0 0 3 0 3 0 1 1 1 0 1 0 0 1

Hi all, As a molecular biologist by training, I'm fairly new to R (and statistics!), and was hoping for some advice. First of all, I'd like to apologise if my question is more methodological rather than relating to a specific R function. I've done my best to search both in the forum and elsewhere but can't seem to find an answer which works in practice. I am carrying out

A new version of the 'snow' package for parallel computing in R is available at http://www.stat.uiowa.edu/~luke/R/cluster/snow_0.3-3.tar.gz This substantially revises the way in which worker processes are started to allow snow to be used on Windows and Mac/Windows/Linux combinations. I have successfully used the SOCK version on a standalone Windows machine and combinations of

I am estimating Value at Risk using PerfomanceAnalytics package. The?variables are stored in a data frame. I formated the data variables using zoo() and as.xtx() but it is not working. The working example is below. ##########################################################? reguire(zoo) require(PerformanceAnalytics) reguire(xts) ? year<- c(1991-12-30, 1992-12-30, 1993-12-30, 1994-12-30) R1

I have the following problem: I am using igraph and entering a graph from an external (.txt) file using read.graph("a.txt","ncol") In a.xtx the name of the nodes contains special characters (such Polish names, etc). In the R console, the visualization of the names is not correct and I am loosing some of the special characters. Any help will be greatly appreciate. Renato

Hi Being able to do object oriented programming in R is really good. I now started using the Reference Classes and really like it. Though, I have one problem: I cannot find a way to update a method on an existing object. The flexibility that scripting gives (really needed for interactive data analysis) is lost if everything have to be recalculated all the time. For example I would normally

I am trying to use knn to do a nearest neighbor classification. I tried using my dataset and got an error message so I used a simple example to try and understand what I was doing wrong and got the same message. Here is what I typed into R: try [,1] [,2] [,3] [,4] r "A" "A" "T" "G" r "A" "A" "T" "G" f

I'd like to fit linear models on very large datasets. My data frames are about 2000000 rows x 200 columns of doubles and I am using an 64 bit build of R. I've googled about this extensively and went over the "R Data Import/Export" guide. My primary issue is although my data represented in ascii form is 4Gb in size (therefore much smaller considered in binary), R consumes about

Hello R users! I have several data frames where some of the variables have many missing observations. For example, Q1 in one of my data frames has over 66% of its observations missing. I have tried imputation with mice but it does not work for all the data frames and I get the following message or a similar message to this: iter imp variable 1 1 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11

Hello all, A few years ago, I added the inalloca feature to LLVM IR so that Clang could be C++ ABI compatible with MSVC on 32-bit x86. The feature works, but there is room for improvement. I recently took the time to write up a design using token values that will hopefully be better named and easier to work with and around. For the technical details of the proposal, I've written up the RFC

Hi! We operate a 2-node cluster running OCFS2 on top of DRBD. It shows about 4.3 GB free space on the OCFS2 filesystem using df on both nodes, but one node can't even write 10 MB: df (ouput identical on both the nodes) $ df -k /cluster Filesystem 1K-blocks Used Available Use% Mounted on /dev/drbd0 83883484 80071096 3812388 96% /cluster $ df -i /cluster

I am using 2.2.0 If the QR decomposition of an N*M matrix is such that the pivoting order is not 1:M, Q%*%R does not result in the original matrix but in a matrix with the columns permuted. This is clearly intentional, and probably to be expected if pivoting is used --- chol() behaves in the same manner (it would perhaps be nice if the qr help page made that clear in the same way that the chol()

I cleaned up my old benchmarking code and added checks for missing data to compare various ways of finding OLS regression coefficients. I thought I would share this for others. the long and short of it is that I would recommend ols.crossprod = function (y, x) { x <- as.matrix(x) ok <- (!is.na(y))&(!is.na(rowSums(x))) y <- y[ok]; x