similar to: Should dots be used in generic functions?

I'm trying to create a package for working on orientation data, i.e. data where the observations are 3D rotations. There are several different representations of orientations in common use: SO(3) matrices, Euler angles, unit quaternions, etc. One thing I'd like is to make it convenient to work in any representation, and have conversions to others done as needed. I'm trying to do

I noticed this oddity about [ and setMethod. First, I define testFunc, which sorts a data frame by the first column and returns the entries that aren't NAs, and testIt, which runs testFunc repeatedly on a random large data frame, each time saving the return into a dummy placeholder (for demonstration's sake). > require(methods) Loading required package: methods [1] TRUE > testFunc

I noticed this oddity about [ and setMethod. First, I define testFunc, which sorts a data frame by the first column and returns the entries that aren't NAs, and testIt, which runs testFunc repeatedly on a random large data frame, each time saving the return into a dummy placeholder (for demonstration's sake). > require(methods) Loading required package: methods [1] TRUE > testFunc

Full_Name: Colin A. Smith Version: 1.8.0 OS: Mac OS X 10.2.6 Submission from: (NULL) (216.102.90.18) Both Biobase and my package annaffy use S4 classes to define methods for "[". Both packages use the save image method of installation. (See annaffy 1.0.3 in BioC CVS.) Depending on how both packages are loaded, the Biobase definitions seem to be getting masked out: >

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

Hello, Thomas Stabla (statho3@web.de) has already sent this question to R-help, Wed, 12 Nov 2003 21:21:31 +0100, but we are not sure whether we should better post this mail to this audience than to R-help: --------------------------------------------------------------------- We are using S4-classes and want to force a validity check when an object is created. How can this be done, when an

I am another person who has had trouble documenting S4 classes and (particularly) methods. The methods package itself is pretty cool by the way, but it is a pity that there are as yet no guidelines on S4 in the "Writing R Extensions" document. I have actually put together a guide on S4 documentation myself for the use of my own lab which is at

Hi! Here a simple example. setClass("myclass" ,representation(info="character") ,contains="matrix" ) rownames(dd)<-c("a","b") tt<-new("myclass",dd) #the source of pain. as(tt,"matrix")<-matrix(1,3,3) Error: length of dimnames [1] not equal to array extent Is there a different way to do what I would like to do (I

Hi! Here a simple example. setClass("myclass" ,representation(info="character") ,contains="matrix" ) rownames(dd)<-c("a","b") tt<-new("myclass",dd) #the source of pain. as(tt,"matrix")<-matrix(1,3,3) Error: length of dimnames [1] not equal to array extent Is there a different way to do what I would like to do (I

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@r-project.org # ###################################################### > x <- rnorm(10) > y <- 1:10 > xyplot(y ~ x) >

Hello, Before I consider purchasing it, can someone comment on the general applicability of John Chambers' book titled "Programming with Data" for R programming. I am currently using R-1.3.1 with the Omega SLanguage and RSMethod packages. Many thanks, Randall -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi, I don't understand why this code doesn't work (f(b2)): ///////////////// setClass("B0", representation(b0 = "numeric")) setClass("B1", representation("B0", b1 = "character")) setClass("B2", representation("B1", b2 = "logical")) f <- function(x) class(x) setMethod("f", "B0",

> setClass('foo') [1] "foo" > setMethod('is.logical', 'foo', function(x) TRUE) [1] "is.logical" > getGeneric('is.integer') Error in options(x) : evaluation is nested too deeply: infinite recursion? > --please do not edit the information below-- Version: platform = i686-pc-linux-gnu arch = i686 os = linux-gnu system =

How would you implement attach()ing to a user-defined class with multiple slots in R? A primary goal of my software is to hide the data representation from the user, allowing them to attach() to my program's complex classes and refer to their contents by name - THEREBY avoiding having to pass the classes to every function call. Also, is it true that attaching to a to a sub-classed list

I have been writing some S4 classes and have a problem about how I might pass a signature to "c()". Take the following example: setClass("collection", representation("list", date="POSIXt")) x <- new("collection", list(1,2,3), date=Sys.time()) y <- new("collection", list(4,5,6), date=Sys.time()) obviously, I can do c(x,y), but

My question has two parts. The first is with regard to the frame or environment in which generic functions are defined in packages. It seems as though they are defined (i.e. exist as objects) in frame 1, even when defined in a package. The following is a short example: setClass("track",representation(x="numeric",y="numeric")) plotTrack <- function(x,y,...)

Full_Name: Tim Keitt Version: 1.5 OS: Linux Submission from: (NULL) (128.227.201.237) The function "setMethod" makes the following call to "insertMethod": allMethods <- insertMethod(allMethods, signature, fnames[1:length(signature)], asMethodDefinition(definition, signature)) If I understand insertMethod correctly, the third argument should be the names of the

In ancient times, circa 1980, S data directories were called swork and sdata not .Data, and "New-S" as described in the already medieval "Blue Book" discusses a function DBCONVERT that converted swork data into .Data data. It is embarrassing to admit it, but I still have data archives from a BTL machine called alice in the swork format and would like to convert a dataset to

I do lots of analyses on large microarray data sets so memory use and speed and both important issues for me. I have been trying to estimate the overheads associated with using formal S4 data objects instead of ordinary lists for large data objects. In some simple experiments (using R 1.7.1 in Windows 2000) with large but simple objects it seems that giving a data object a formal class

Hello! as(object,"list") and as(object,"matrix") behave quite differently if it comes to their attributes. I define two classes. One of them "contains" a "list" the other a "matrix" setClass("myclass" ,representation(info="character") ,contains="matrix" ) setClass("mylist"