similar to: Building a Matrix

Dear List: I'm working to construct a very large sparse matrix and have found relief using the SparseM package. I have encountered an issue that is confusing to me and wonder if anyone may be able to suggest a smarter solution. The matrix I'm creating is a covariance matrix for a larger research problem that is subsequently used in a simulation. Below is the latex form of the matrix if

Dear List: I am having to build a block-diagonal matrix (vl) and am currently using the following code. I<-diag(sample.size) vl<-kronecker(I,vl.mat) This code works fine, but for large N, it is a huge memory hog. Is there a more efficient method for constructing vl? Thanks, Harold [[alternative HTML version deleted]]

Dear List: I am working to construct a matrix of a particular form. For the most part, developing the matrix is simple and is built as follows: vl.mat<-matrix(c(0,0,0,0,0,64,0,0,0,0,64,0,0,0,0,64),nc=4) Now to expand this matrix to be block-diagonal, I do the following: sample.size <- 100 # number of individual students I<- diag(sample.size) bd.mat<-kronecker(I,vl.mat) This

I need to wrap a loop inside a function and am having a small bit of difficulty getting the results I need. Below is a replicable example. # define functions pcm <- function(theta,d,score){ exp(rowSums(outer(theta,d[1:score],'-')))/ apply(exp(apply(outer(theta,d, '-'), 1, cumsum)), 2, sum) } foo <- function(theta,items, score){ like.mat <-

Dear List: A few weeks ago I posted some questions regarding data simulation and received some very helpful comments, thank you. I have modified my code accordingly and have made some progress. However, I now am facing a new challenge along similar lines. I am attempting to simulate 250 datasets and then run the data through a linear model. I use rm() and gc() as I move along to clean up the

Dear List: I have a question regarding an MDS procedure that I am accustomed to using. I have searched around the archives a bit and the help doc and still need a little assistance. The package isoMDS is what I need to perform the non-metric scaling, but I am working with similarity matrices, not dissimilarities. The question may end up being resolved simply. Here is a bit of substantive

Suppose I have a square matrix P P <- matrix(c(.3,.7, .7, .3), ncol=2) I know that > P * P Returns the element by element product, whereas > P%*%P Returns the matrix product. Now, P^2 also returns the element by element product. But, is there a slick way to write P %*% P %*% P Obviously, P^3 does not return the result I expect. Thanks, Harold [[alternative HTML version

List: Thank you for the replies to my post yesterday. Gabor and Phil also gave useful replies on how to improve the function by relying on mapply rather than the explicit for loop. In general, I try and use the family of apply functions rather than the looping constructs such as for, while etc as a matter of practice. However, it seems the mapply function in this case is slower (in terms of CPU

Dear List: I am running into a memory issue that I haven't noticed before. I am running a simulation with all of the code used below. I have increased my memory to 712mb and have a total of 1 gb on my machine. What appears to be happening is I run a simulation where I create 1,000 datasets with a sample size of 100. I then run each dataset through a gls and obtain some estimates. This works

Hi R-Help! I am trying to find a nicer way of extracting all the "complete" diagonals of a matrix. I am working with very large matrices that have many more rows than columns. I want to be able to extract each of the diagonals that are as long as the number of columns in the matrix. I have written a rather ugly function that presently does the job. It illustrates what I am trying to

I'm trying to update my Matrix package given the update last night. But the following error is generated. I've tried restarting R and deleting my old Matrix package. Can anyone suggest how this might be resolved? > install.packages('Matrix') trying URL 'http://www.ibiblio.org/pub/languages/R/CRAN/bin/windows/contrib/2.1/Mat rix_0.98-1.zip' Content type

I have a function that I am currently using very inefficiently. The following are needed to illustrate the problem: set.seed(12345) dat <- matrix(sample(c(0,1), 110, replace = TRUE), nrow = 11, ncol=10) mis <- sample(1:110, 5) dat[mis] <- NA theta <- rnorm(11) b_vector <- runif(10, -4,4) empty <- which(is.na(t(dat))) So, I have a matrix (dat) with some values within the matrix

Dear List: First, many thanks to those who offered assistance while I constructed code for the simulation. I think I now have code that resolves most of the issues I encountered with memory. While the code works perfectly for smallish datasets with small sample sizes, it arouses a windows-based error with samples of 5,000 and 250 datasets. The error is a dialogue box with the following: "R

The following 1460 x 1460 matrix can be throught of as 16 distinct 365 x 365 matrices. I'm trying to set off-diaganol terms in the 16 sub-matrices with indices more than +/- 5 (days) from each other to zero using some for loops. This works well for some, but not all, of the for loops. The R code I"m using follows. For some reason the third loop below zero's-out everything in the

Hi all, hope someone can help me out with this. Background Introduction I have a data set consisting of data collected from a questionnaire that I wish to validate. I have chosen to use confirmatory factor analysis to analyse this data set. Instrument The instrument consists of 11 subscales. There is a total of 68 items in the 11 subscales. Each item is scored on an integer scale between 1 to 4.

Hi, Is there a function for raising a matrix to a power? For example if you like to compute A%*%A%*%A, is there an abbreviation similar to A^3? Atte Tenkanen > A=rbind(c(1,1),c(-1,-2)) > A [,1] [,2] [1,] 1 1 [2,] -1 -2 > A^3 [,1] [,2] [1,] 1 1 [2,] -1 -8 But: > A%*%A%*%A [,1] [,2] [1,] 1 2 [2,] -2 -5

Hello, I am looking for a way to obtain standard errors for emprirical Bayes estimates of a model fitted with lmer (like the ones plotted on page 14 of the document available at http://www.eric.ed.gov/ERICDocs/data/ericdocs2/content_storage_01/0000000b/80/2b/b3/94.pdf). Harold Doran mentioned (http://tolstoy.newcastle.edu.au/~rking/R/help/05/08/10638.html) that the posterior modes' variances

Dear all I have the following problem. M <- matrix(0,3,3) # dimension of M is dinamic and this can lead to the next subscript diag(M[1,1]) <- rep(100,1) #Error in `diag<-`(`*tmp*`, value = 100) : # only matrix diagonals can be replaced diag(M[1,1,drop=F]) <- rep(100,1) #Error in diag(M[1, 1, drop = F]) <- rep(100, 1) : # incorrect number of subscripts diag(M[2:3,1:2]) <-

Hi I have some panel data for the 50 US states over about 25 years, and I would like to test a simple model via OLS, using this data. I know how to run OLS in R, and I think I can see how to create Panel Corrected Standard Errors using http://jackman.stanford.edu/classes/350C/pcse.r What I can't figure out is how to correct for autocorrelation over time. I have found a lot of R stuff on

Dear List: When I click on the link to download a reference manual for a package on cran, I get an error message that the file is damaged and could not be repaired. I randomly chose various packages and the same error message appears. Are the links actually broken? I have also restarted my machine and closed and re-opened acrobat. I am using Windows XP, Acrobat Professional 6.0.0.5, and