similar to: difference in rbind() [print?] behavior between 2.2.1 and 2.3.0

Dear R-Core, after switching to 2.3.0, all my trusted do.call constructs that worked in 2.2 and earlier fail. I noted that changes were introduced to do.call, but I could not find out how these relate to my problem. The following example works in 2.2 and earlier, but fails because rownames are partially NA. I can correct this by manually adding row names, but it's a bit of work to check this

Any help on the following would be appreciated... > R.version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 2 minor 2.1 year 2005 month 12 day 20 svn rev 36812 > language R > set.seed(2) > x=matrix(ncol=4, nrow=10) > for(i in 1:4) x[,i]=rnorm(10) > x=as.data.frame(x) > x$V1=c(rep(0,5), rep(1,5)) >

Hi [macOSX 10.4.6; R-2.3.0] I have encountered a difference in behaviour between R-2.2.1 and R-2.3.0 when performing a linear model. Transcript follows for R-2.3.0 (R-2.2.1 worked as expected). How to make R-2.3.0 perform as R-2.2.1 did? > dput(x) c(29.13, 29.88, 30.09, 29.99, 29.74, 29.64, 29.65, 29.7, 30.04, 29.89, 29.96, 29.65, 28.76, 28.41, 28.38, 29.55, 29.76, 29.75, 29.84,

Hallo all, I have the following glm model: f1 <- as.formula(paste("factor(y.fondi)~", "flgsess + segmeta2 + udm + zona.geo + ultimo.prod.", "+flg.a2 + flg.d.na2 + flg.v2 + flg.cc2", " +(flg.a1 + flg.d.na1 + flg.v1 + flg.cc1)^2", " + flg.a2:flg.d.na2 + flg.a2:flg.v2 +

Dear all I am trying to compare the performances of several methods using the AUC0.1 and not the whole AUC. (meaning I wanted to compare to AUC's whose x axis only goes to 0.1 not 1) I came to know about the Mcneil Hanley test from Bernardo Rangel Tura and I referred to the original paper for the calculation of "r" which is an argument of the function cROC. I can only find the

Dear all, I try to compare the performances of several parameters to diagnose lameness in dogs. I have several ROC curves from the same dataset. I plotted the ROC curves and calculated AUC with the ROCR package. I would like to compare the AUC. I used the following program I found on R-help archives : From: Bernardo Rangel Tura Date: Thu 16 Dec 2004 - 07:30:37 EST

I might be missing something but I thought that AUC was a measure for comparing ROC curves, so there is nothing else needed to "compare" them. The larger AUC is the higher correlation of 2 variables compared. No other measures or calculations are needed. Jarek Tuszynski -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On

Dear all, I want to compute the average of the three blocks for each x-variable which is equal slide in the code below. How can I do that ? block x1 x2 x3 x4 x5 1 23 22 23 24 23 1 21 25 26 21 39 1 23 24 22 23 23 2 20 21 23 24 28 2 32 23 34 24 26 2 19

I've moved this from r-help to r-bugs. If you reply, please be careful that replies go to the right place: r-bugs if your comment is specifically about the bug (and it contains the PR# in the subject that will be added when this is cc'd to r-devel), r-devel if general discussion, not both. On Wed, 14 Jul 2004 10:01:45 -0400, "Roger D. Peng" <rpeng@jhsph.edu> wrote :

Okay, looking at the docs, then it's not a bug, since the "..." argument is not actually documented as "other arguments passed to f or grad". However, that *is* how it's document in `optim', so one can see how this might cause some confusion. Now, it's not clear to me which other arguments need to be passed to `optim' except perhaps `hessian'. Am

Full_Name: Hin-Tak Leung Version: R 2.2.1 OS: x86_64-redhat-linux-gnu Submission from: (NULL) (131.111.186.92) rbind/cbind is unimplemented for raw (RAWSXP) types. I have a working patch implementing the functionality, to follow. --please do not edit the information below-- Version: platform = x86_64-redhat-linux-gnu arch = x86_64 os = linux-gnu system = x86_64, linux-gnu status = major

I've noticed a change in the way grep() behaves between the 1.9.0 release and a recent R-patched. On 1.9.0 I get the following output: > x <- dget(file = url("http://www.biostat.jhsph.edu/~rpeng/names.R")) > length(grep("^l\\w+tmean", x, perl = TRUE, value = TRUE)) [1] 84 And on R-patched (2004-06-11) I get > x <- dget(file =

Dear R developers, We have a big SGI Origin computation server with 32 cpu's and 64 Gb of RAM. In R 2.0.0 we could run large jobs, allocating 8 Gb of RAM was not a problem, for example by running: > v1 <- seq(1,2^29) > v2 <- seq(1,2^29) > v3 <- seq(1,2^29) > v4 <- seq(1,2^29) This yields an R process, consuming about 8 Gb of RAM: PID PGRP USERNAME

hello r development team, i'm building R 2.3.0 on solaris and when i run the 'make install' i'm getting a syntax error during the "installing etc ..." which causes the installation to fail. i get this error whether i use gnu-make of sun-make, see the error and reasons below. gmake[1]: Entering directory `/export/medusa/darin/build/R-2.3.0/etc' installing etc ...

hi all, is there a new mechanism in R 2.3.0 for copying libraries from, say, R 2.2.1 to R 2.3.0? i ask because gabor grothendieck comments in his copydir.bat (from gabor's batchfiles at: http://cran.r-project.org/contrib/extra/batchfiles/batchfiles_0.2-5.zip ): ``:: I personally upgraded my 2.1.0 to 2.2.0 this way so it seems ok until :: R replaces this with something better which is

I noticed today that in R 2.3.1, I get > serialize(list(1,2,3), NULL, ascii = TRUE) [1] "A\n2\n131841\n131840\n19\n3\n14\n1\n1\n14\n1\n2\n14\n1\n3\n" > but in R 2.4.0 alpha I get > serialize(list(1,2,3), NULL, ascii = TRUE) [1] 41 0a 32 0a 31 33 32 30 39 36 0a 31 33 31 38 34 30 0a 31 39 0a 33 0a 31 34 [26] 0a 31 0a 31 0a 31 34 0a 31 0a 32 0a 31 34 0a 31 0a 33 0a >

Dear all. RE how to estimate parameters of multimodal distribution Thank to prof.Ripley for pointing me to mclust package, although I am not sure I can apply it to my problem. I have another question. I need to change some of my values in data frame to NA. I use something like df[df$v1 < 5, 5:10] <- NA which is OK if there are no NA values in v1. here are some foo attempts >

Hello everybody, if I try to (r)bind a number of large dataframes I run out of memory because R wastes memory and seems to "forget" to release memory. For example I have 10 files. Each file contains a large dataframe "ds" (3500 cols by 800 rows) which needs ~20 MB RAM if it is loaded as the only object. Now I try to bind all data frames to a large one and need more than

Hello, I have a data set with a grouping variable (TRIPID) and several other variables. TRIPID is repeated in some areas and I would like to use a function like aggregate to sum the variable UNITS according to TRIPID. However I would also like to retain the other variables as they are in the data set with the new summed TRIPID. So what I have is something like this: YEAR MONTH DAY

Hello, I have a data set with a grouping variable (TRIPID) and several other variables. TRIPID is repeated in some areas and I would like to use a function like aggregate to sum the variable UNITS according to TRIPID. However I would also like to retain the other variables as they are in the data set with the new summed TRIPID. So what I have is something like this: YEAR MONTH DAY