similar to: stats lm() function

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 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

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 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

I can't find a way to retrieve z values and p-values from the output from lmer in the lme4 package. How is this done? Rick B.

Hi, I have been having a tedious issue with lmer models with lots of factors and lots of levels. In order to get the basic information at the beginning of the print out I also have to generate these enormous tables as well. Is there a method command to leave off all of the effects and correlations? Or, do I have to go to string commands?

Hello All, I would like to retrieve some of the results from the lmer(...) function in library lme4. If I run a model, say fm.1 <- lmer(y ~ 1 + (1 | x), data = dog) and try names(fm.1), I get NULL. Is there anyway to retrieve the information? Thanks

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 LLVM assembler by appending characters to >> the >> end of a sequential

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

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)

Hi, (R version 2.15.0) I am running a pgm with 1 response (earlier standardized Y) and 44 independent vars (Xi) from the same data =a2: When I run the 'lm' function on single Xi at a time, the beta coefficient for let's say X1 is = -0.08 (se=0.03256) But when I run the same Y with 44 Xi-s with the 'step' function (because I left direction parameter empty, I assume a backward

Dear folks, I?m trying to build a function to create and make available some variables I frequently use for testing purposes. Suppose I have a function that takes some inputs and creates (internally) several named objects. Say, fun1 <- function(x, y, z) {obj1 <- x; obj2 <- y; obj3 <- z <missing stuff> } Here is the challenge: After I run it, I want the objects to be

Hi, I read the example in http://wiki.rubyonrails.com/rails/pages/HowtoUseMultipleDatabases, it show us how to connect to other database, each time we start a new action, however, this doesn''t work while you try to connect to two different database within the same action. SO I wonder is it possible to bind to two or more database within the same action???? Thanks you very much Saiho

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 everyone, have a small problem trying to converting a dataset in matrix form to an array. Specifically: data include 3D measurement -x,y,z of 59 points in 36 objects. They are stored as a matrix (x) of 2124 rows and 3 columns. What I want to do is to extract each subject's dataset using an array (b). Accordingly, I tried the following command: b<-array(a,c(59,3,36)). The problem is

Dear useRs, I have a function that creates several global objects with assign("obj",obj,.GlobalEnv), and which I need to run iteratively in another function. The code is similar to f <- function(...) { assign("obj",obj,.GlobalEnv) } fct <- function(...) { for (i in 1:1000) { ... f(...) ...obj... rm(obj) #code fails without this line } } I don't understand

Hello, all. I'm new to R and just beginning to learn to write functions. I know I'm out of my depth posting here, and I'm sure my issue is mundane. But here goes. I'm analyzing the American National Election Study (nes), looking at mean values of a numeric dep_var (environ.therm) across values of a factor (partyid3). I use ddply from plyr and wtd.mean from Hmisc. The nes requires a

Hi, is there a way to reference to a data object without copying it? For example I have a huge matrix called dist and I want two objects obj1 and obj2 to have a memeber dist that points to the matrix, but I don't want, for memory reasons, to copy the matrix twice. As far as I understand the following code will generate three copies of my data: dist <- some_code_that_generates_data

I have a large R program that I am constantly running over and over again. At the beginning of this program, I create a hige matrix and a huge dataframe but these are constant. What I mean by constant is that, if I run the program over later, I really should just use the old matrix and dataframe ( if they exist ) that were created in a previous run so that the program doesn't have to spend

Dear all, My double for loop as follows, but it is little efficient, I hope all friends can give me a "vectorized" program to replace my code. thanks x: is a matrix 202*263, that is 202 samples, and 263 independent variables num.compd<-nrow(x); # number of compounds diss.all<-0 for( i in 1:num.compd) for (j in 1:num.compd) if (i!=j) { S1<-sum(x[i,]*x[j,])