search for: crossproduct

Hello, I am working on a variable selection problem and I wonder whether there is some function or package in R works similar to the 'PROC REG' in SAS? Thank you. Some facts about 'PROC REG': PROC REG in SAS first composes a crossproducts matrix. The matrix can be calculated from input data, reformed from an input correlation matrix, or read in from an SSCP data set. For each model, the procedure selects the appropriate crossproducts from the main matrix. The normal equations formed from the crossproducts are solved by using a swee...

...the matrix with the eigenvectors V usually are not normalized to the length of 1, but in the way that the following holds (SPSS does the same thing): t(Vstar)%*%Derror%*%Vstar = I where Vstar are the normalized eigenvectors. Derror is an "error" or "within" squaresum- and crossproduct matrix (squaresum of the p variables on the diagonale, and the non-diagonal elements are the sum of the crossproducts). For Derror the following holds: Dtotal = Dtreat + Derror. Since I assume that many of you are familiar with this transformation: can anybody of you tell me, how to conduct this t...

...claimed by allocVector. Even after applying gc() function and removing the R object created by the .Call function, the memory was still not reclaimed back to the operating system. Here is an example. It was modified from the convolve2 example from the R extension manual. Now I am computing the crossproduct of a and b, which returns a vector of size length(a)*length(b). The C code is at the end of this message with the modification commented. The R code is here ---------------------------- dyn.load("crossprod2.so") cp <- function(a, b) .Call("crossprod2", a, b) gctorture() a&l...

...claimed by allocVector. Even after applying gc() function and removing the R object created by the .Call function, the memory was still not reclaimed back to the operating system. Here is an example. It was modified from the convolve2 example from the R extension manual. Now I am computing the crossproduct of a and b, which returns a vector of size length(a)*length(b). The C code is at the end of this message with the modification commented. The R code is here ---------------------------- dyn.load("crossprod2.so") cp <- function(a, b) .Call("crossprod2", a, b) gctorture() a&l...

Dear list, how can I compute the inverse of the X'WX matrix ("inverse of the weighted sum of squares and crossproducts matrix") from an object of class "lm" from a weigthed linear regression? Thanks, Sven

Hi all, I need to minimize following function : dat <- matrix(rnorm(20000), ncol=2) targetFn <- function(x) { dat <- as.matrix(dat) dat1 <- 1*dat[,1] - (x^2)*dat[,2] return(sd(dat1)) } i.e. I want ro find for which "x" the value of "targetFn" will be minimum, depending on current dataset "dat". Is there any

Hi All, (I have a degree in math, but I am too embarassed to ask my colleagues, so here goes:) I would like to get a vector normal (orthogonal) to a plane formed by two other vectors. In matlab I do this: v1 = [.4, .6, .8]; v2 = [.9, .7, .2]; nn = cross(v1,v2) (gives ~[-.48, .65, -.24] if I do R> cross(v1, v2), I get .94. Huh? Thanks for all your help, again. W

We have a problem inverting a matrix which has the following eigenvalues: > eigen(tcross, only.values=TRUE) $values [1] 7.917775e+20 2.130980e+16 7.961620e+13 8.241041e+12 2.258325e+12 [6] 3.869428e+11 6.791041e+10 2.485352e+09 9.863098e+08 9.819373e+05 [11] 3.263408e+05 2.929853e+05 2.920419e+05 2.714355e+05 8.733435e+04 [16] 8.127136e+04 6.543883e+04 5.335074e+04

Dear All, I have a problem that I think could be solved much more efficiently, but I don't have a clue how to accomplish this. I have a matrix W with dimensions k*(p+1): Let's say W is 5*4 and looks like this: > W [,1] [,2] [,3] [,4] [1,] 1 -1 -1 1 [2,] 1 1 1 1 [3,] 1 2 -2 -1 [4,] 1 0 -1 -1 [5,] 1 -2 -1 0 I want to take

Hi, Is in R any "fast" algorithm for correlation? What I mean is: I have very large dataset (microarray) with 55000 rows and 100 columns. I want to count correlation (p-value and cor.coef) between each row of dataset and some vector (of course length of this vector is equal to number of columns of dataset). In short words: For t-test we have: "normal" algorithm - t.test

Hi guys, I am still learning R, and not well familiar with all the apply functions. I am trying to find faster alternatives to replace the for cycle. Is there a faster way to do the example below? nm <- 1000 b <- matrix (rnorm (5000, 0, 1), nrow = 500, ncol = nm) a <- matrix (0, nm, nm) for (i in 1 : nm) { for (j in 1 : nm) { if ( j == i) { next } a[i, j] <- t (b [, i]) %*% b[, j] } }

Hi, I have a huge number of categorical variables, say at least 10000, and I put them into a matrix, each column is one variable. The question is: how can I make all of the pairwise cross tabulation tables efficiently? The straightforward solution is to use for-loops by looping two indexes on the table() function, but it was just too slow. Is there a more efficient way to do that? Any guidance

Dear R-experts! I have y(x) data, dim(y) == dim(x) == c(2000, 2) I'd like to fit them with nls: fit.result <- nls ( y ~ f(x, p1, p2, p3), start = list(p1 = ... , p2 = .. , p3 = ..) ) Actually I want to fit y[,1] ~ x[,1] and y[,2] ~ x[,2] *simulaneously*, with the same parameters set {p1, p2, p3}. I tried to feed R tha above formula, R errors with:

...rged, so anyone > considering further explorations with it may need training in > Rchaeology. A few hours ago I submitted to CRAN a package "biglm" that does large linear regression models using a similar strategy (it uses incremental QR decomposition rather than accumalating the crossproduct matrix). It also computes the Huber/White sandwich variance estimate in the same single pass over the data. Assuming I haven't messed up the package checking it will appear in the next couple of day on CRAN. The syntax looks like a <- biglm(log(Volume) ~ log(Girth) + log(Height), chunk1)...

Dear All, I have written the following programs to find a non-singular (10*10) covariance matrix. Here is the program: nitems <- 10 x <- array(rnorm(5*nitems,3,3), c(5,nitems)) sigma <- t(x)%*%x inverse <- try(solve(sigma), TRUE) while(inherits(inverse, "try-error")) { x <- array(rnorm(5*nitems,3,3), c(5,nitems)) sigma <- t(x)%*%x inverse <-

I'm trying to get R running on AIX 5.1 with the native AIX compilers, VisualAge C++ 5.0.2.0 and XL Fortran Compiler 7.1.0.0. R compiled fine, but in running the test cases, the base-Ex.R tests fail in the Choleski decomposition with the following: > x <- matrix(c(1:5, (1:5)^2), 5, 2) > m <- crossprod(x) > Q <- chol(m) Error in chol(m) : NA/NaN/Inf in foreign function call

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 I'm trying to understand some C code in an R package I'm using. I'm address this question here as it's matrix algebra...and I'm no pro at that! the C command reads: double alpha = 1.0, beta = 0.0; dsyrk_("L", "N", nGenes, nGenes, & alpha, mat1, nGenes, & beta, mat2, nGenes); - From google,

I am pretty new to R. So this may be an easy question for most of you. ? I would like to calculate the squared distances of a large set (let's say 20000) of vectors (let's say dimension of 5) to a fixed vector. ? Say I have a data frame MY_VECTORS with 20000 rows and 5 columns, and one 5x1 vector y. I would like to efficiently calculate the squared distances?between each of the 20000

Dear all, Can any one tell me how can i perform Orthogonal Polynomial Regression parameter estimation in R? -------------------------------------------- Here is an "Orthogonal Polynomial" Regression problem collected from Draper, Smith(1981), page 269. Note that only value of alpha0 (intercept term) and signs of each estimate match with the result obtained from coef(orth.fit). What

This question comes up periodically, probably enough to give it a proper thread and maybe point to this thread for reference (similar to the 'conservative anova' thread not too long ago). Moving from lme syntax, which is the function found in the nlme package, to lmer syntax (found in lme4) is not too difficult. It is probably useful to first explain what the differences are between the