similar to: Matrix Column Names

Hi, I have two (large) matrices A and B of dimensions (m,n) and (p,n) respectively. I'd like to see if the is a fast way to compute a new matrix C with dimension (m*p,n) in which each row in C is found by applying some function f to each pair of rows (x,y) where x is a row in A and y is a row in B. For example, if A <- matrix(c(1, 2, 3, 4, 5, 6), byrow=TRUE, ncol=3) B <- matrix(c(7,

Hi, I have a list of vectors (of varying lengths). I'd like to sort this list by applying a function to each pair of vectors in the list and returning information to sorting routine that let's it know which one is larger. To solve problems like this in Common Lisp, the sort function accepts a function as an argument. The arguments to this function are two elements of the list which is

Dear R users, I'm having some problems with the stack() and unstack() functions, and wondered if you could help. I have a large data frame (400 rows x 2000 columns), which I need to reduce to a single column of values (and therefore 800000 rows), so that I can use it in other operations (e.g., generating predictions from a GLM object). However, the problem I'm having can be reproduced

Hi all. I have unexpected reshape results on datasets with certain variable names. Here a reproducible example: d <- matrix(seq_len(7*7), 1, 7*7) vnames <- c('acc','ppeGross','CF','ROA','DeltaSales','invTA','DeltaRevDeltaRec') varying <- unlist(lapply(vnames, paste, 1:7, sep='.')) d <- data.frame(d) names(d) <- varying

Hi, I noticed that paste and expression in the following manner does not produce the desired result. vnames=c("pi","y","delta") vx=vn=3 m=16 # mydata any matrix that takes for the following form mydata=array(somedata,c(m,vn,vx)) # producing plots for (j in 1:vx){ for (i in 1:vn) { plot(mydata, type="l", main=expression(paste("Effect of",

I'm trying to use Rprof() to identify bottlenecks and speed up a particullary slow section of code which reads in a portion of a tif file and compares each of the values to values of predictors used for model fitting. I've written up an example that anyone can run. Generally temp would be a section of a tif read into a data.frame and used later for other processing. The first portion

Dear List, I have a large (1600*1600) matrix generated with symnum, that I am using to eyeball the structure of a dataset. I have abbreviated the column names with the abbr.colnames option. One way to get an even more compact view of the matrix would be to display the column names rotated by 90 degrees. Any pointers on how to do this would be most useful. Any other tips for displaying the

Is there code available to decompose interactions involving at least one nominal factor with more than 2 levels as described, e.g., by Tukey or by Mandel (1971, Technometrics, 13: 1-18)? Tukey's model: E(y[i,j]) = mu0 + a[i] + b[j] + c*a[i]*b[j], estimating a, b, and c so sum(a) = sum(b)= 0. Mandel essentially describes a singular value decomposition of the interaction. Thanks,

Dear all (copying the package author), I have a question on the vlmc package. I am trying to model a time series, where each element can take one of 11 values (the result of some clustering). When I run the following command (synthetic data to facilitate self-contained example) I get the following warning: ("alphabet with >1-letter strings; trying to abbreviate") +++ START+++ >

I have searched the archives and instructions high and low but have not found what I need. I have a dataframe named Data with columns: AdjResND0 - the numeric result Parameter - the classification (chemical name) Sort - the order I want the chemical names to appear, with leading zeros so all are the same length Client.Name - the sample name I am trying to generate a series of 1 page barcharts,

Hi, I am trying to do multinomial regression using the glmnet package, but the following gives me an error (for no reason apparent to me): library(glmnet) cv.glmnet(x=matrix(c(1,2,3,4,5,6,1,2,3,4,5,6), nrow=6),y=as.factor(c(1,2,1,2,3,3)),family='multinomial',alpha=0.5, nfolds=2) The error i get is: Error in if (outlist$msg != "Unknown error") return(outlist) : argument is of

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this is a bad question but I can't figure it out and i've tried. if i sort the 2 column matrix , temp1, by the first column, then things work as expected. But, if I sort the 1 column matrix, temp2, then it gets turned coerced to a vector. I realize that I need to use drop=FALSE but i've put it in a few different places with no success. Thanks. temp1 <-

I am trying to analyse some data and was given R code to do this with but there seem to be errors in the code. My level of knowledge is improving but still limited. The details are; Data on clover lines; Lines.txt attached. Comma seperations Code: options(digits=3) clover <- read.table("Lines.txt",header=T,sep=",") vnames <- names(clover);nv <- length(vnames)

Hi, I'm very new to working with sparse matrices and would like to know how I can column permute a sparse matrix. Here is a small example: > M1 <- > spMatrix(nrow=5,ncol=6,i=sample(5,15,replace=TRUE),j=sample(6,15,replace=TRUE),x=round_any(rnorm(15,2),0.001)) > M1 5 x 6 sparse Matrix of class "dgTMatrix" [1,] 2.983 . 1.656 5.003 . . [2,] .

When i make Boxplots with a lot of boxes, the names of them get only written down every second "column". Since they aren't in any way ordered, you don't see anymore to what they belong. example: l<-rep(list(1:5),20); boxplot(l,names=sample(20,1:20)) Is there a way to show them all, or do i have to split the plots? [[alternative HTML version deleted]]

Hi, Starting with ordinary vectors, so we know what to expect: > mapply(function(x, y) {x * y}, 101:106, rep(1:3, 2)) [1] 101 204 309 104 210 318 > mapply(function(x, y) {x * y}, 101:106, 1:3) [1] 101 204 309 104 210 318 Now with an S4 object: setClass("A", representation(aa="integer")) a <- new("A", aa=101:106) > length(a)

Hello, I have multiple data files. Each file contains a single column and 1.5 million rows. I want to create normalized pdfs (area under curve is 1) and histograms to compare with one another. Could anybody suggest if there exists an easy way or built in function in R. At present I am using Origin and Excel together to do this. A single file needs 10 minutes and I have a total of 929 files! So

Hi, I've had a look the bug list and searched though the R documentation, email lists etc. but didn't see anything on this: when I do: summary(species.glm1, correlation = TRUE) I get a correlation matrix like this: Correlation of Coefficients: ( p I(H C pH * 1 I(pH^2) * B 1 Ca . . 1 I(Ca^2) . . B attr(,"legend") [1] 0 ` ' 0.3 `.'