similar to: outer fails with group generic operations on factors (PR#166)

The inconsistency is that you use $<- to set the column, then [[<- to change it. Had you tried to set the column by x[[1]] <- as.character(x[[1]]) you would have seen the problem immediately (it does not work as you would have intended). If you want to be sure to turn off conversion to factor, you need to set the column to class "AsIs". My belief is that will behave

(1) ?cbind claims The generic functions `cbind' and `rbind' take a sequence of vector and/or matrix arguments and combine them as the columns or rows, respectively, of a matrix. Note: The method dispatching is not done via `UseMethod(..)', but by C-internal dispatching. Therefore, there's no need for, e.g., `rbind.default'. but my cbind.ts

> From: Peter Dalgaard BSA <p.dalgaard@biostat.ku.dk> > > Martin Maechler <maechler@stat.math.ethz.ch> writes: > > > Reading the long output of ``cc -flags'', > > I see that more than average optimization is done using > > cc -xO[1-4] > > # And -O is -xO2. If you do want higher speed, you need to use other flags too, and -fast is a

The Complex group generic for S4 methods is not working: > setClass('foo', representation(z='complex')) [1] "foo" > setMethod('Complex', 'foo', function(z) callGeneric(z@z)) [1] "Complex" > Arg(new('foo', z=1+0i)) Error in Arg(new("foo", z = 1 + (0+0i))) : non-numeric argument to function > The fix is

The TITLE for the nnet package is garbled: it comes out as nnet Feed-forward neural networks and multinomial log-linear nnet Feed-forward neural networks and multinomial log-linear models The problem is in maketitle: auk% cat DESCRIPTION Bundle: VR Version: 6.1-9 Date: 2000/07/11 Depends: R (>= 1.1) Author: S original by Venables & Ripley. R port by Brian Ripley

As far as I can see sys.on.exit() does not work. The following will give a non-null result in S, and seems about the simplest possible usage. test <- function() { on.exit(print(1)) ex <- sys.on.exit() str(ex) cat("exiting...\n") } > test() NULL exiting... [1] 1 Looking at the code suggests that on.exit is installing the code in a different context from the one

Using zero-weight values in glm returns incorrect fitted values and linear predictors, the ninth value in the following. > example(glm) > fit <- glm(counts ~ outcome + treatment, family = poisson(), data=d.AD, weights=c(rep(1,8), 0)) > fit$linear.predictor 1 2 3 4 5 6 7 8 2.989646 2.535391 2.862201 2.989646

anova() on three or more objects behaves inconsistently in R. In R anovalist.lm does a sequential ANOVA using pairwise F tests, ignoring all the other objects, so the larger of the two models provides the denominator. In S anova.lmlist uses the denominator from the largest model (smallest residual df) in the set, as does anova.glmlist in both. I suggest that R's anovalist.lm is wrong (that

On Wed, 30 Jul 2008, Martin Maechler wrote: >>>>>> "RobMcG" == McGehee, Robert <Robert.McGehee at geodecapital.com> >>>>>> on Tue, 29 Jul 2008 15:40:37 -0400 writes: > > RobMcG> FYI, > RobMcG> I've tried posting the below message twice to the bug tracking system, > > [....... r-bugs problems discussed in a

predict.lm has been broken by recent changes to the patched branch. It fails for all singular fits. An example: library(MASS) data(quine) quine.hi <- aov(log(Days + 2.5) ~ .^4, quine) quine.nxt <- update(quine.hi, . ~ . - Eth:Sex:Age:Lrn) predict(quine.nxt) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 NA NA NA NA NA NA NA NA NA NA NA NA NA

I am not sure if this is a bug within is.na<- or if it lies deeper in the dataframe construction process. Indeed, perhaps it is not a bug at all (in which case I would suggest that the help page for NA be provided with a warning for unsuspecting users (like me)). When used on a character vector within a dataframe, is.na<- coerces the vector to factor. > x <- data.frame(var =

NAME Douglas Bates EMAIL bates@stat.wisc.edu VERSION 0.63.3 PLATFORM i386-unknown-linux SYSTEM Debian 2.1 CC/FC/MAKE egcs/g77/make NAME Martyn Plummer EMAIL plummer@iarc.fr VERSION 0.63.3 PLATFORM i386-unknown-linux SYSTEM Redhat 5.1 CC/FC/MAKE gcc/egcs-g77/make NAME Göran Broström EMAIL gb@stat.umu.se VERSION 0.63.3 PLATFORM

Version: platform = sparc-sun-solaris2.6 arch = sparc os = solaris2.6 system = sparc, solaris2.6 status = status.rev = 0 major = 0 minor = 64.2 year = 1999 month = July day = 3 language = R -- It is unclear to me that the handling of NAs is desirable, and it has problems with names: > z <- c(T,T,NA,F,T) > names(z) <- letters[1:5] > which(z) Error: names attribute

Hi all, I am building a R package which defines a class. I have overloaded methods for this class using setMethod, and now, when I require my package, I get diagnostic messages. setMethod("[", signature(x = "portfolio"), function(x, i, j, ...) { # ... }) > require(portfolio) Loading required package: portfolio [... loading other required packages ...] Creating a new

> -----Original Message----- > From: llvm-dev [mailto:llvm-dev-bounces at lists.llvm.org] On Behalf Of Tim > Northover via llvm-dev > Sent: Wednesday, July 25, 2018 3:07 AM > To: Nat! > Cc: LLVM Developers Mailing List > Subject: Re: [llvm-dev] A question to the DWARF experts on symbol > indirection > > Hi Nat!, > > On Wed, 25 Jul 2018 at 01:21, Nat! via

Hello, I am having trouble specifying a suitable nlme model. My data structure is described by gd <- groupedData(ppath ~ lcut | exp, outer = ~ bait, inner = ~ weight, data = d) i.e. the response (ppath) of several subjects (sub) was measured at levels of a continuous variable (lcut). Subjects were given either of one level of a factor (bait), and all subjects were measured at two levels

I use knncat to make a predictive model and get misclass rate > knncat.m<-knncat(training.new,k=c(10,20),classcol=5) > knncat.m Training set misclass rate: 36.88% then I try to calculate prediction accuracy by the following: > pr.knncat.train <- predict (knncat.m,training.new,training.new,train.classcol=5,newdata.classcol=5) > tb.knncat.train <-table (pr.knncat.train,

An NA in the rownames of a matrix (or dataframe) causes misalignment when the matrix is printed: R> x <- matrix(1:12, 3,4, dimnames=list(letters[1:3], LETTERS[1:4])) R> rownames(x)[2] <- NA R> x A B C D a 1 4 7 10 <NA> 2 5 8 11 c 3 6 9 12 The bug is in function Rstrlen, in src/main/printutils.c. MatrixRowLabel and MatrixColumnLabel (same file) rely on Rstrlen

Just after R-0.63.0 came out I reported a problem to the list. I was unable to get ./configure to complete it's job, because it couldn't find a working fortran. It stopped after trying to test for underscore in subroutine names. This was especially perplexing, since I could still configure the older version (I had 62.2) with no error messages. I tried suggestions of B.D. Ripley and

Hello, When I merge two dataframes, each created by read.table and containing no factors, the result has columns which have been converted to factor. I think this is a bug (and may be related to some fixed bugs, eg PR#1102, PR#1121), but will await comments before submitting. Also, I'm not sure if the bug is in merge() or in read.table(). I am using R-1.4.0 patch 1/13/02 on Solaris 2.6.