similar to: Too generic with S4 methods?

setClass("B", representation=representation("B", extra="numeric)) setClass("B", representation=representation(extra="numeric"), contains="B") Are these the same? If not, how do they differ? What about setClass("B", representation=representation("B", extra="numeric"), contains="B") ? As far as I can

I wonder if someone would confirm something I'm 99% sure of from the docs and discussion on the list, but can't find stated explicitly: R works only on problems that fit into (real or virtual) memory. Thus, even if you have a problem (e.g., simple regression) that could be solved by doing some operation on each row of a dataset at a time, you can't solve it unless the entire dataset

I defined an S4 class with a slot i. Then I wrote a regular function that attempted to increment i. This didn't work, apparently because of the general rule that a function can't change the values of its arguments outside the function. I gather there are ways around it, but the Green book admonishes "cheating on the S evaluation model is to be avoided" (p. 190). Thinking that

I am trying to package some code that is a tweak to the survival package. When I asked earlier, the list consensus was that it would be best to do this as a separate package, dependent on survival. This is proving a bit tricky. I have some run-time and compile time concerns. Run-time, my R code needs R code from survival, and my C code needs C functions from survival. Will this all be

I'd like to have a class A that computes a likelihood, and a subclass B that computes the same likelihood by sometimes throws in an additional term (B includes measurement error). So B's likelihood needs to call A's, and then (sometimes) multiply by an additional term. It sounds as if, in the S3 scheme, NextMethod is supposed to do this: like.A <- function(stuff) compute value

I have code and documentation that don't match, but R CMD check didn't flag it. in mspath.R mspath <- function(formula, # formula with observed Markov states ~ observation times (required) qmatrix, # matrix of 1s and 0s with indices of allowed transitions (diagonal is ignored) (required) misc = FALSE, ematrix = NULL, # matrix

I am calling a C (C++ really) function via the .C interface. Sometimes when things go wrong I want to return an error message. 1. R provides C functions error and warning which look about right. But exactly how does this exit, and in particular what happens with cleaning up, calling C++ destructors, and unwinding the stack? Will I get memory leaks? 2. Before I discovered those functions, I

I have 3 questions about creating a package for R, and would appreciate any guidance. 1. Demonstrating the packaged code requires some support code files. It would seem natural to put these in the demo directory, but "Writing R extensions" says files in demo should be suitable for runing from demo(). The support files are not intended to be run from the top level. How should I handle

I thought there were some gaps in the R Extensions document; in particular, I was left wondering how to create a list. I think a paragraph on it would be useful. I would be happy to contribute the paragraph, but I'm not sure if there's interest or what the procedure is. Can anyone advise me? Though I was looking at the 1.7.0 version, I just checked 1.8.0 and the relevant section seems

I want to add an argument if it is not present. Following the Green Book, p. 337: test <- function(x, ...){ dots <- list(...) if (!hasArg(from)) from <- 0 else from <- dots$from curve(x, from=from, ...) } > test(sin) > test(sin, from=4) Error in curve(x, from = from, ...) : formal argument "from" matched by multiple actual arguments The FAQ says, in

When I give plot some bad paramaters, it keeps giving me error messages forever after. I think the last time this happened, I even got the error messages for completely unrelated, non-graphical functions. Here's a recent example: > plot(it[[31]][,c(1, 3)], type="b", usr=c(0, 20, -20, 5)) Warning messages: 1: parameter "usr" couldn't be set in high-level plot()

There are a few points I found unclear or unmentioned in the snow documentation (mostly I looked at the cluster.html web page). I thought I'd mention them here. What is the start up environment for the children? -------------------------------------------------- My best guess at the answer is in parentheses Do they inherit shell variables? (no) Do they inherit variables set in R or other

I want to maximize a conditional likelihood function that is basically logistic conditional on the number of successes within strata. What would be a good starting place for this? A complication is that the denominator includes a term that is the sum over all permutations. Although there is no time dimension to the problem, it's possible a degenerate use of the Cox proportional hazards

I have written an R package, mostly implemented in C++. It works. I want to do unit tests of the C++ code, as well as higher level tests from R. I use the boost test framework for this, part of which is a library which wants to be the thing that starts everything running (i.e., main). My code has a few invocations of R functions--just error at the moment, I think. The list might grow. So I

I am sure that I do not have the answer to your question - but I would like to add to your views regarding the S4-issue. I've been involved in the gRbase package for graphical modelling in R. The first version was implemented in S4 but now we have made a roll-back to using S3 methods. While, I can see that S3 is not really comprehensive enough for 'proper' object oriented

I can't tell from the docs ("Writing R Extensions" 1.9.1) exactly what environment the tests, examples, and vignettes that R CMD check tries to run are in. In particular: 1) how do I get the package loaded? 2) how do I access data in the data/ directory? 3) where is the material in the other directories? (e.g., has inst/ material been installed? where?) Apparently (section 1.3)

I've run into an unpleasant oddity involving the interaction of environments and generic functions. I want to check my diagnosis, and see if there is a good way to avoid the problem. Problem: A library defines "foo" <- function(object) 1 setMethod("foo", c("matrix"), function(object) 30) After loading the library foo(0) is 1 foo(matrix()) is 30 foo is a

I think I now understand how R looks up names. Could anyone tell me if I have this right? First it looks up the nested environments created by lexical scoping. Then, if it gets to the top (.GlobalEnv) it also looks through the list of things that have been "attach"ed. It never looks in the call stack unless you explicitly ask it to, or mess with the environment frames. The reason I

Originally I made a function yearStop that took an argument "object". I made a generic, but later changed the argument to "x". R keeps resurrecting the old definition. Could anyone explain what is going on, or how to fix it? Note particularly the end of the transcript below: I remove the generic, verify that the symbol is undefined, make a new function, and then make a

I have a likelihood I would like to compute using C++ and then optimize. It has data that need to persist across individual calls to the likelihood. I'd appreciate any recommendations about the best way to do this. There are several, related issues. 1. Use the R optimizer or a C optimizer? Because of the persistence problems (see below), using a C optimizer has a certain attraction.