similar to: linear model benchmarking

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

Dear R experts: I just tried some simple test that told me that hand computing the OLS coefficients is about 3-10 times as fast as using the built-in lm() function. (code included below.) Most of the time, I do not care, because I like the convenience, and I presume some of the time goes into saving a lot of stuff that I may or may not need. But when I do want to learn the properties of an

Hi all, I am using R trying to get a inverse matrix of (X^T)X , but I keep getting the error message like: no b argument and no default value for sprintf(gettext(fmt, domain = domain), ...) . -------------------------------------------------------------------------------------------- # my code X<-Matrix(rep(1,500),100,5) X[lower.tri(X)]<-1-10^-7 XtX<- t(X)%*% X XtXu<-lu(XtX)

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

Dear List: I am having some trouble reproducing some GLS estimates using matrix operations that I am not having with other R procedures. Here are some sample data to see what I am doing along with all code: mu<-c(100,150,200,250) Sigma<-matrix(c(400,80,16,3.2,80,400,80,16,16,80,400,80,3.2,16,80,400),n c=4) sample.size<-100 temp <-

On Jun 12, 2008, at 11:38, Hendrik Boom wrote: > On Tue, 06 May 2008 16:06:35 -0400, Gordon Henriksen wrote: > >> On 2008-05-06, at 13:42, Hendrik Boom wrote: >> >>> One more question. I hope you're not getting tired of me already. >>> Does generating LLVM code have to proceed in any particular order? >>> >>> Of course, if I am writing

>> >> I think I may have found an exception to this -- the API seems to >> require me to have all the fields for a struct ready before I >> construct the struct. I don't have the ability to make a struct >> type, use it to declare some variables, and still contribute fields >> to it during the rest of the compilation. >> >> Is there a

Hi, all: I want to check small files' property(such as date, path, and so on) frequently. The files are stored in netwrok driver and their sizes vary from 2KB to 5KB. I found that Windows 2K outperform Linux/Samba very much after I campared the bench results. I am very confused about it and who can explain it? The computers' configurations are as follows: 1. PC Client It

Hi, Im using the lm() function where the formula is quite big (300 arguments) and the data is a frame of 3000 values. This is running in a loop where in each step the formula is reduced by one argument, and the lm command is called again (to check which arguments are useful) . This takes 1-2 minutes. Is there a way to speed this up? i checked the code of the lm function and its seems that its

Hi Martin I got stung by this last week. glmnet produces a coefficient matrix of class ?dgCMatrix? If a predictor matrix was created using sparseMatrix as follows, one gets unexpected results, as this simple example shows. My fix was easy (I always convert the predictor matrix to class ?dgCMatrix? now) Trevor > y=Matrix(diag(4)) > y 4 x 4 diagonal matrix of class "ddiMatrix"

Hi Martin, package arules heavily relies on ngCMatrix and uses multiplication and addition for logical operations. I think it makes sense that in a mixed operation with one dgCMatrix and one ngCMatrix the ngCMatrix should be "promoted" to a dgCMatrix. The current behavior of %*% and friends is in deed confusing: > m <- matrix(sample(c(0,1), 5*5, replace=TRUE), nrow=5) >

Dear R-help, I have a four-part question about regression, matrices, and sandwich package. 1) In the sandwich package, I would like to better understand the meat() function. >From the bread() documentation, for a simple OLS regression, bread() returns (1/n * X'X)^(-1) That is, for a simple regression (per the documentation on bread()): MyLM <- lm(y ~ x) bread(MyLM)

Hi! I have used the Matrix package (Version: 0.995-10) successfully to obtain the OLS solution for a problem where the design matrix X is 44000x6000. X is very sparse (about 80000 non-zeros elements). Now I want to do WLS: (X'WX)^-1X'Wy I tried W=Diagonal(length(w),w) and wX=solve(X,W) but after various minutes R gives a not enough memory error (Im using a 64bit machine with 16Gigs

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()

Skipped content of type multipart/alternative-------------- next part -------------- Index: pbx.c =================================================================== RCS file: /usr/cvsroot/asterisk/pbx.c,v retrieving revision 1.14 diff -u -r1.14 pbx.c --- pbx.c 19 Apr 2003 02:41:22 -0000 1.14 +++ pbx.c 21 Apr 2003 02:27:43 -0000 @@ -713,6 +713,8 @@ { char *first,*second; char tmpvar[80] =

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

Dear R users, I'm trying to carry out monte carlo integration of a posterior density function which is the product of a normal and a gamma distribution. The problem I have is that the density function always returns 0. How can I solve this problem? Here is my code #generate data x1 <- runif(100, min = -10, max = 10) y <- 2 * x1^2 + rnorm(100) # # # # # # # # Model 0 # # # # # # #

Dear addresses, I need perform a batch of 10 000 simulations for each of 4 options considered. (The idea is to obtain the parameter estimates in a heteroskedastic linear regression model - with additive or mixed heteroskedasticity - via the Kenward-Roger small-sample adjusted covariance matrix of disturbances). For this purpose I wrote an R program which would capture all possible options (true

I found out the other day that crossprod() will take a single matrix argument; crossprod(x) notionally returns crossprod(x,x). The two forms do not return identical matrices: x <- matrix(rnorm(3000000),ncol=3) M1 <- crossprod(x) M2 <- crossprod(x,x) R> max(abs(M1-M2)) [1] 1.932494e-08 But what really surprised me is that crossprod(x) is slower than crossprod(x,x): R>