search for: forwardsolve

...ror gives: > > traceback() 9: .C("bakslv", t = r, ldt = nrow(r), n = k, b = x, ldb = k, nb = nb, x = matrix(0, k, nb), job = job, info = integer(1L), DUP = FALSE, PACKAGE = "base") 8: backsolve(l, x, k = k, upper.tri = upper.tri, transpose = transpose) 7: forwardsolve(vh, a, upper.tri = TRUE, transpose = TRUE) 6: as.matrix(x) 5: svd(forwardsolve(vh, a, upper.tri = TRUE, transpose = TRUE)) 4: glssoln(w, x, b) 3: FUN(X[[1L]], ...) 2: lapply(dat, function(x) glssoln(w, x, b)) 1: brown(logflatnodes, archotreeouch) >

...ion > and overview of this package. > Help for individual functions is also obtained by adding the > suffix '.spam' to the function name, e.g. 'help( chol.spam)'. > Attaching package: ‘spam’ > The following object(s) are masked from ‘package:base’: > backsolve, forwardsolve > > x=read.MM("/Users/brad/Dropbox/graphchi/2008.csv_global_mean.mm") > Error in read.MM("/Users/brad/Dropbox/graphchi/2008.csv_global_mean.mm") > : > invalid mode (list) to pass to Fortran (arg 8) [[alternative HTML version deleted]]

...spam) for some demos, help( Spam) for an overview of this package. Help for individual functions is optained by adding the suffix '.spam' to the function name, e.g. 'help(chol.spam)'. Attaching package: ?spam? The following object(s) are masked from ?package:base?: backsolve, forwardsolve, norm While I understand that it's helpful to see these message at times, most of the time I already knew these information, especially when I run things from a script. The bigger problem is that it clutters up the output and makes it harder to notice if there are real problems. I have tried...

...m{bdsmatrix}{\code{signature=(r= "gchol.bdsmatrix")} for the generalize cholesky decomposition of a bdsmatrix object} } } \value{ The solution of the triangular system. The result will be a vector if \code{x} is a vector and a matrix if \code{x} is a matrix. Note that \code{forwardsolve(L, b)} is just a wrapper for \code{backsolve(L, b, upper.tri=FALSE)}. } \description{ The generalized Cholesky decompostion of a symmetric matrix A is \eqn{A = LDL'}{A= LD t(L)} where D is diagonal, L is lower triangular, and \eqn{L'}{t(L)} is the transpose of L. These functions s...

...mos, help( Spam) for an overview of this package. Help for individual functions is optained by adding the suffix '.spam' to the function name, e.g. 'help(chol.spam)'. Attaching package: 'spam' The following object(s) are masked from 'package:base': backsolve, forwardsolve, norm Try help(fields) for an overview of this library fields web: http://www.image.ucar.edu/Software/Fields Loading required package: mapproj Loading required package: maps Loading required package: snow Loading required package: tcltk Loading Tcl/Tk interface ... done ooooooooooooooooooooooooo...

...mos, help( Spam) for an overview of this package. Help for individual functions is optained by adding the suffix '.spam' to the function name, e.g. 'help(chol.spam)'. Attaching package: 'spam' The following object(s) are masked from 'package:base': backsolve, forwardsolve, norm Try help(fields) for an overview of this library fields web: http://www.image.ucar.edu/Software/Fields Loading required package: mapproj Loading required package: maps Loading required package: snow Loading required package: tcltk Loading Tcl/Tk interface ... done ooooooooooooooooooooooooo...

Just wondering if there's an R package which does tricks similar to what TK!Solver does. TK!Solver, for those not lucky enough to have found it, basically allows one to define a bunch of equations, assign values to an arbitrary subset of the variables, from which it calculates (either directly when possible or back-solving when not) the values of the other variable(s). thanks Carl

...olve' JonR> methods for triangular matrices and variance matrices -- would JonR> you be interested? { Jonathan, I hope it's okay if I CC this to R-devel; this must be of a wider interest } Ye.e..s; for triangular ones, there's the 4 different ways of backsolve()/forwardsolve().. Well, we (R core) have discussed matrix classes months and years ago.. and haven't really started doing anything about it. I think we then agreed to follow Splus' Matrix library and use class "Matrix" (and classes inheriting from "Matrix", i.e. class == c("...

...ols.cholesky = function (y, x) { x <- as.matrix(x) ok <- (!is.na(y))&(!is.na(rowSums(x))) y <- y[ok]; x <- subset(x, ok) x <- cbind( 1, x) ch <- chol( crossprod(x) ) backsolve(ch, forwardsolve(ch, crossprod(x,y), upper=TRUE, trans=TRUE)) } ) set.seed(0) y <- matrix(rnorm(N*MC), N, MC) x <- array(rnorm(MC*K*N), c(N, K, MC)) cat("N=", N, "K=", K, " (MC=", MC, ")") if (K>1) { sum.cor <- 0 for (mc in 1:MC) {...

...[B]] SX <- A2%*%SU%*%t(A2) ## Now, compute correlation matrix between X and Z C1 <- matrix(0,q,q) F <- row(C1)-col(C1)+1 E <- (F>=1) & (F<=p+1) C1[E] <- tphi[F[E]] g <- matrix(0,q,1) if (q) g[1:q,1] <- ttheta[1:q] rXZ <- forwardsolve(C1,g) SXZ <- matrix(0, p, q+1) F <- col(SXZ)-row(SXZ) E <- F>=1 SXZ[E] <- rXZ[F[E]] S <- rbind(cbind(SX,SXZ),cbind(t(SXZ),diag(q+1))) } Q0 <- A%*%S%*%t(A) } The second way is to resolve brutally the equation of Gardner et al. in the form (12),...

Hi the manpage says that crossprod(x,y) is formally equivalent to, but faster than, the call 't(x) %*% y'. I have a vector 'a' and a matrix 'A', and need to evaluate 't(a) %*% A %*% a' many many times, and performance is becoming crucial. With f1 <- function(a,X){ ignore <- t(a) %*% X %*% a } f2 <- function(a,X){ ignore <-

...nless na.last = NA. o diag() as used to generate a diagonal matrix has been re-written in C for speed and less memory usage. It now forces the result to be numeric in the case diag(x) since it is said to have 'zero off-diagonal entries'. o backsolve() (and forwardsolve()) are now internal functions, for speed and support for large matrices. o More matrix algebra functions (e.g. chol() and solve()) accept logical matrices (and coerce to numeric). o sample.int() has some support for n >= 2^31: see its help for the limitations....

...nless na.last = NA. o diag() as used to generate a diagonal matrix has been re-written in C for speed and less memory usage. It now forces the result to be numeric in the case diag(x) since it is said to have 'zero off-diagonal entries'. o backsolve() (and forwardsolve()) are now internal functions, for speed and support for large matrices. o More matrix algebra functions (e.g. chol() and solve()) accept logical matrices (and coerce to numeric). o sample.int() has some support for n >= 2^31: see its help for the limitations....