similar to: s4 generic issue

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

This issue ties loosely into other recent S4 topics on this board. The methods package defines a number of implicit generics for linear algebra related functions (rcond, norm, backsolve) that, when used, interfere with base package operations. Here is the cut-and-paste version of the code the illustrates the problem: # rcond x1 <- cbind(1, 1:10) rcond(x1) setGeneric("rcond")

I get the following error from one of my Rd files in R CMD check (R 2-14.0) * checking Rd files ... WARNING Error in switch(attr(block, "Rd_tag"), TEXT = if (!grepl("^[[:space:]]* $", : EXPR must be a length 1 vector problem found in ?backsolve.Rd? This is likely something that will be glaringly obvious once it's pointed out, but without a line number I can't

Hi, I have just installed R 2.4.0 and when I try to load SpareseM, I get the following error message library(SparseM) Package SparseM (0.71) loaded. To cite, see citation("SparseM") Error in loadNamespace(package, c(which.lib.loc, lib.loc), keep.source = keep.source) : in 'SparseM' methods specified for export, but none defined: as.matrix.csr, as.matrix.csc,

Dear R users I am trying to obtain p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and the command summary. > > My problems is that p values derived from both these methods are totally different. My question is (1) there a bug in my code and > (2) How can I proceed, left with these uncertainties in the estimations of > the p-values? > > Below

Dear all, I am currently analyzing count data from a hierarchical design, and I?ve tried to follow the suggestions for a correct estimation of p-values as discusssed at R-Wiki (http://wiki.r-project.org/rwiki/doku.php?id=guides:lmer-tests&s=lme%20and%20aov). However, I have the problem that my model only consists of parameters with just 1 d.f. (intercepts, slopes), so that the

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

(To the list moderator: I just subscribed to the list. Apologies for not having done so longer before trying to post.) Hi all, I am currently using lmer to analyze data from an experiment with a single fixed factor (treatment, 6 levels) and a single random factor (block). I've been trying to follow the online guidance for estimating p-values for parameter estimates on these and other

Dear R users I am trying to obtain p-values for (quasi)poisson lmer models, using Markov-chain Monte Carlo sampling and the command summary. > > My problems is that p values derived from both these methods are totally different. My question is (1) there a bug in my code and > (2) How can I proceed, left with these uncertainties in the estimations of > the p-values? > > Below is

I need to find solutions to a tridiagonal system. By this I mean a set of linear equations Ax = d where A is a square matrix containing elements A[i,i-1], A[i,i] and A[i,i+1] for i in 1:nrow, and zero elsewhere. R is probably not the ideal way to do this, but this is part of a larger problem that requires R. In my application it is much easier (and much faster) to generate the diagonal and

I'm trying to use stepAIC on sparse matrices, and I need some help. The documentation for slm.fit suggests: slm.fit and slm.wfit call slm.fit.csr to do Cholesky decomposition and then backsolve to obtain the least squares estimated coefficients. These functions can be called directly if the user is willing to specify the design matrix in matrix.csr form. This is often advantageous in large

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;

I've put up R-0.63.1.tgz up for FTP from Auckland some minutes ago. As usual, don't get it from there unless you are desperate, but wait for it to be mirrored at a CRAN site near you within a day or two. For those who *are* desperate, I've left a copy in ftp://blueberry.kubism.ku.dk/pub/R-devel/R-0.63.1.tgz (Be gentle, that's my desktop PC!) The person who fetched R-0.63 *from

I've put up R-0.63.1.tgz up for FTP from Auckland some minutes ago. As usual, don't get it from there unless you are desperate, but wait for it to be mirrored at a CRAN site near you within a day or two. For those who *are* desperate, I've left a copy in ftp://blueberry.kubism.ku.dk/pub/R-devel/R-0.63.1.tgz (Be gentle, that's my desktop PC!) The person who fetched R-0.63 *from

Hello All, I was wondering if it's possible to reshape data from long to wide in R without using a "timevar". I've pasted some sample data below along with some code. The data are sorted by Subject and Drug. I want to transpose the Drug variable into multiple columns in alphabetical order. My data have a variable called "RowNo" that functions almost like a

Hi, New to R, using version 2.0.1 (2004-11-15) on debian Linux (sid), kernel 2.6.8-2-686. I have data in files with separate vectors on each row of the file, e.g., $ cat /tmp/stats freq,0,1,2,3,4,5,6,7,8,9,16,17,18,19,20,... noise,49,47,48,48,50,47,48,47,46,50,48,54,49,47,49,... signal,99,0,100,0,0,100,0,100,100,0,100,101,100,0,0,... pctrcv,5,0,5,0,0,5,0,5,11,0,5,5,5,0,0,...

Hello everybody When one is working with complex matrices, "transpose" very nearly always means *Hermitian* transpose, that is, A[i,j] <- Conj(A[j,i]). One often writes A^* for the Hermitian transpose. I have only once seen a "real-life" case where transposition does not occur simultaneously with complex conjugation. And I'm not 100% sure that that wasn't a

I decided to do a direct comparison of transpose and sweep. library(microbenchmark) NN <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, byrow = TRUE) # Example matrix lambda <- c(2, 3, 4) # Example vector colNN <- t(NN) microbenchmark( sweep = sweep(NN, 2, lambda, "/"), transpose = t(t(NN)/lambda), colNN = colNN/lambda ) Unit: nanoseconds expr min lq

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 +

Hi All, I want to ask if there is a transpose function for data frame like the procedure of transpose in SAS? Because I want to partially transpose a data frame which contains 5 columns (siteid, date, time, obs, mod), what I want to do is to put time as the column variables along with siteid, and date, and put obs and mod in the row names. specifically to transpose a data frame: siteid date