similar to: Solving a tridiagonal system

Dear R Users, I am trying to solve a tridiagonal matrix in R. I am wondering if there is an inbuilt R function or package to solve that. I tried looking on google but couldn't find something that would help directly. Any help is highly appreciated. Thanks. Janesh [[alternative HTML version deleted]]

I've added a "backsolve" method to the bdsmatrix library. Per the Extending manual section 7.1 I've also added the following 3 lines along with my setMethod definitions for 2 classes. backsolve <- function(r, ...) UseMethod("backsolve") backsolve.default <- base:::backsolve formals(backsolve.default) <- c(formals(backsolve.default), alist(... = )) I've

After an ajax insertion in the dom, it seems the browser don''t refresh its knowledge of the page. The source of the page is identical after the insertion, whereas the display is correct. The ajax insertion is used to add an input field. The added fields are ignored by the submission of the form. Isn''t it possible to do that, or maybe there''s a problem in my code

I'm hoping that someone with deeper insight into S4 than I, that is to say virtually everyone reading this list, could help resolve the following problem in SparseM. We have setGeneric("backsolve", function(r, x, k = NULL, upper.tri = NULL, transpose = NULL, twice = TRUE, ...) standardGeneric("backsolve"), useAsDefault= function(r, x,

Hi, I am generating large Kac matrices (also known as Clement matrix). This a tridiagonal matrix. I was wondering whether there is a vectorized solution that avoids the `for' loops to the following code: n <- 1000 Kacmat <- matrix(0, n+1, n+1) for (i in 1:n) Kacmat[i, i+1] <- n - i + 1 for (i in 2:(n+1)) Kacmat[i, i-1] <- i-1 The above code is fast, but I am curious about

Hi, I am trying to convert the following NLMIXED code to NLME, but am running into problems concerning 'Singularity in backsolve'. As I am new to R/S-Plus, I thought I may be missing something in the NLME code. NLMIXED *********** proc nlmixed data=kidney.kidney; parms delta=0.03 gamma=1.1 b1=-0.003 b2=-1.2 b3=0.09 b4=0.35 b5=-1.43 varu=0.5; eta=b1*age+b2*sex+b3*gn+b4*an+b5*pkn+u;

Hi, I am trying to solve a model that consists of rather stiff ODEs in R. I use the package ODEsolve (lsoda) to solve these ODEs. To speed up the integration, the jacobian is also specified. Basically, the model is a one-dimensional advection-diffusion problem, and thus the jacobian is a tridiagonal matrix. The size of this jacobian is 100*100. In the original package

Hi, I am hoping some one can help with this. I am using nlme to fit a random coefficients model. It ran for hours before returning Error: Singularity in backsolve at level 0, block 1 The model is > plavix.nlme<-nlme(PLX_NRX~loglike(PLX_NRX,PD4_42D,GAT_34D,VIS_42D,MSL_42D,SPE_ROL,XM2_DUM,THX_DUM,b0,b1,b2,b3,b4,b5,b6,b7,alpha), + data=data, + fixed=list(b0 +

>>>>> "JonR" == Jonathan Rougier <J.C.Rougier@durham.ac.uk> writes: JonR> ... By the way, I have `solve' 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,

Hi, I have sparse (tridiagonal) matrices, and I use the Matrix package for handling them. For certain computations, I need to either set the first row to zero, or double the last row. I find that neither operation preserves sparsity, eg > m <- Diagonal(5) > m 5 x 5 diagonal matrix of class "ddiMatrix" [,1] [,2] [,3] [,4] [,5] [1,] 1 . . . . [2,] . 1

Dear R-Help list: I'm using the Matrix library to operate on 600 X ~5000 element unsymmetrical sparse arrays. So far, so good, but if I find I need more speed or functionality, how hard would it be to utilize other sparse matrix toolsets from within R, say MUMPS, PARDISO or UMFPACK, that do not have explicit R interfaces? More information on these is available here

Hi I'm trying to use ouch's hansen and brown functions but I get the error: > brown(logflatnodes,archotreeouch) Error in backsolve(l, x, k = k, upper.tri = upper.tri, transpose = transpose) : NA/NaN/Inf in foreign function call (arg 1) and with hansen also: Error in optim(par = c(sqrt.alpha, sigma), fn = function(par) { : function cannot be evaluated at initial parameters

Hi, For another trainingset I get this error message, which again is rather cryptic to me: Setting default kernel parameters Error in array(0, c(n, p)) : 'dim' specifies too large an array RMate stopped at line 0 of selection Calls: rvm ... .local -> backsolve -> as.matrix -> chol -> diag -> array thanks for any suggestions!

Hi all, I would like to fit a gamm model of the form: Y~X+X*f(z) Where f is the smooth function and With random effects on X and on the intercept. So, I try to write it like this: gam.lme<- gamm(Y~ s(z, by=X) +X, random=list(groups=pdDiag(~1+X)) ) but I get the error message : Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1

I am trying to run a GLMM on some binomial data. My fixed factors include 2 dichotomous variables, day, and distance. When I run the model: modelA<-glmmPQL(Leaving~Trial*Day*Dist,random=~1|Indiv,family="binomial") I get the error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 >From looking at previous help

Dear R-help, Why can't lme cope with an incomplete whole plot when analysing a split-plot experiment? For example: R : Copyright 2006, The R Foundation for Statistical Computing Version 2.3.1 (2006-06-01) > library(nlme) > attach(Oats) > nitro <- ordered(nitro) > fit <- lme(yield ~ Variety*nitro, random=~1|Block/Variety) > anova(fit) numDF denDF F-value

Hi, We have data of two groups of subjects: 32 elderly, 14 young adults. for each subject we have 15 observations, each observation consisting of a reaction-time measure (RT) and an activation maesure (betadlpcv). since we want to analyze the influence of (age-)group and RT on the activation, we call: lme(betadlpcv ~ RT*group, data=our.data, random=~ RT |subject) this yields: Error in

https://bugzilla.mindrot.org/show_bug.cgi?id=2027 Priority: P5 Bug ID: 2027 Assignee: unassigned-bugs at mindrot.org Summary: SSH generates misleading errors when using public key authentication Severity: normal Classification: Unclassified OS: Linux Reporter: xavier.jodoin at corp.ovh.com

I'm trying to read data a program produces in matrixmarket array format into R and its giving me fits. I've tried read.MM (below) and readMM (from the Matrix package) but neither works. One of them says array format isn't supported, the other reports something indecipherable about Fortran. Here's the file contents. Can't get much simpler than that! %%MatrixMarket matrix array

Dear all R users, Can anyone tell me to calculate Eigen value of any real symmetric matrix which algorithm R uses? Is it Jacobi method ? If not is it possible to get explicit algorithm for calculating it? Thanks and regards, Arun [[alternative HTML version deleted]]