search for: nvars

Dear Thomas & Dear List, the fitting function `coxph.fit' called by `coxph' may fail to estimate the regression coefficients when some values of the design matrix are very large. For example library(survival) ### load example data load(url("http://www.imbe.med.uni-erlangen.de/~hothorn/coxph_fit.Rda")) method <- "efron" ### copied from `coxph.fit' coxfit

Dear All, I am trying to solve the optimization problem below, but I am always getting the following error: Error in optim(rep(20, nvar), f, gr, method = "L-BFGS-B", lower = rep(0, : L-BFGS-B needs finite values of 'fn' Any ideas? Thanks in advance, Paul ----------------------------------------------- k <- 10000 b <- 0.3 f <- function(x) { n <- length(x)

Background: I administer a cluster of RedHat EWS 3U4 Linux workstations at a university. I built R 2.1.0 from source: ./configure \ --prefix=/sscc/opt/R-2.1.0 \ --with-blas=no \ 2>&1 \ | tee NUInstall.configure R is now configured for i686-pc-linux-gnu Source directory: . Installation directory: /sscc/opt/R-2.1.0 C compiler:

...NULL, 3 mustart = NULL, offset = rep(0, nobs), family = gaussian(), 4 control = glm.control(), intercept = TRUE) 5 { 6 x <- as.matrix(x) 7 xnames <- dimnames(x)[[2]] 8 ynames <- names(y) 9 conv <- FALSE 10 nobs <- NROW(y) 11 nvars <- NCOL(x) 12 if (nvars == 0) { 13 cc <- match.call() 14 cc[[1]] <- as.name("glm.fit.null") 15 return(eval(cc, parent.frame())) 16 } 17 if (is.null(weights)) 18 weights <- rep(1, nobs) 19 if (is.null(offset)) 2...

R CMD check tells me * checking R code for possible problems ... NOTE agexact.fit.rds: ... may be used in an incorrect context: ?optim(init, agfitfn, ...)? Warning: <anonymous>: ... may be used in an incorrect context: ?optim(init, agfitfn, ...)? Can anyone tell me what this message means? My searches haven't turned up anything useful. This is with R 2.7 and 2.9. The message

A number of users have spotted a terminal problem with the 'debug' package under R2.4.0, along the lines of > mtrace(x) > x() Error in attr(value, "row.names") <- rlabs : row names must be 'character' or 'integer', not 'double' This arose from a bug in 'rbind.data.frame' in R2.4.0 itself. The bug is fixed in R2.4.0 patched, so the

Hi, I'm currently using the R package e1071 to train naive bayes classifiers and came across a bug: When the posterior probabilities of all classes are small, the result from the predict.naiveBayes function become NaNs. This is an issue with the treatment of the log-transformed probabilities inside the predict.naiveBayes function. Here is an example to demonstrate the problem (you might need

When attempting to merge 3 data frames, one of which has fewer columns than the others, rbind.data.frame correctly refuses to perform the bind. However, the error message given is a bit obscure due to a logical bug in the match.names() internal function to rbind.data.frame. Illustration: ## Three data frames with same column variable names: > foo <- data.frame(v1 = c('a',

Hi, in R-3.0 I get the following error when calling lsfit with more observations than variables, which seems to come from an error in the formatting of the error message (note that this was not happening in 2.15.3): > nobs <- 5; nvar <- 6; lsfit(matrix(runif(nobs*nvar), ncol=nvar), runif(nobs), intercept=FALSE) Error in sprintf(ngettext(nry, "%d response", "%d

I am now getting the occasional complaint about survival routines that are not able to handle big data.?? I looked in the manuals to try and update my understanding of max vector size, max matrix, max data set, etc; but it is either not there or I missed it (the latter more likely).?? Is it still .Machine$integer.max for everything??? Will that change??? Found where? I am going to need to go

Hallo All, I would like to apply a function to all permutations of variables in a dataframe (except the first). What is the best way to achieve this? I produce the permutations using: nvar <- ncol(dat) - 1 perms <- as.matrix( expand.grid(rep( list(1:0) , nvar ))[ , nvar:1] ) Thanks in advance Serguei Test-dataframe, comma-delimited: code,wav,w,area,gdp,def,pop,coast,milspend,agr

Dear R community members and R experts I am stuck at a point and I tried with my colleagues and did not get it out. Sorry, I need your help. Here my data (just created to show the example): # generating a dataset just to show how my dataset look like, here I have x variables # x1 .........to X1000 plus ind and y ind <- c(1:100) y <- rnorm(100, 10,2) set.seed(201) P <-

hi all i know that one should try and limit the amount of looping in R programs. i have supplied some code below. i am interested in seeing how the code cold be rewritten if we dont use the loops. a brief overview of what is done in the code. ============================================== ============================================== ============================================== 1. the input

Assume a C program invoked by .Call, that returns a list. Near the top of the program we allocate space for all the list elements. (It is my habit to use "xyz2" for the name of the R object and "xyz" for the pointer to its contents.) PROTECT(means2 = allocVector(REALSXP, nvar)); means = REAL(means2); PROTECT(u2 = allocVector(REALSXP, nvar)); u =

Hi, I want to run t.test() for several variables among two groups, and I would like to skip the tedious process of collecting information to assemble a table, but I am not sure if the function I want already exists. Any suggestion would be appreciated. I have a working example, as required by the posting guide: my_swiss = swiss[-1,] my_swiss$facto = rep(1:2,nrow(my_swiss)/2)

Dear All I am using rgenoud to solve the following maximization problem: myfunc <- function(x) { x1 <- x[1] x2 <- x[2] if (x1^2+x2^2 > 1) return(-9999999) else x1+x2 } genoud(myfunc, nvars=2, Domains=rbind(c(0,1),c(0,1)),max=TRUE,boundary.enforcement=2,solution.tolerance=0.000001) How can one increase the precision of the solution $par [1] 0.7072442 0.7069694 ? I have tried solution.tolerance but without a significant improvement. Any ideas? Thanks in advance, Paul

Hello Dear R community,  This is my problem.  I have a data set (dataframe) called "mydat". It consist of 3 numerical variable.  They are Centrecode, FSUSN and Round. I want to create unique ID by combining these 3 variables. Follwing commands gives me what I need. mydat1 <- paste(mydat$Centrecode, mydat$FSUSN,mydat$Round,sep="") newds <- data.frame(mydat1)    For a

Hello, I will use the lca method in the e1071 package. But I get the following error: Error in pas[j, ] <- drop(exp(rep(1, nvar) %*% log(mp))) : number of items to replace is not a multiple of replacement length Does anybody know this error and knows what this means? Kind regards, Tryntsje

Hi, I would like to connect my GHA-system to R and to deliver the library to users of R. could you give me a comprehensive example containing the step by step procedure to: 1. create a callable library with R binaries (not having a standalone version of R with its own main()). If possible, I would like to run the system on unix,alpha,linux computers as well as on IBM's parallel

Hi, I sent the below message to r-devel but received no answer. I would like to contribute my system but I need som advice concerning the connections. Is my proposal of any interest to you? regards Ralf ?stermark ----- Forwarded message from Ralf ?stermark <rosterma at abo.fi> ----- Date: Mon, 21 Feb 2005 11:35:21 +0200 From: Ralf ?stermark <rosterma at abo.fi> Reply-To: Ralf