similar to: Plot can't forget bad parameters

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 tried the following (relevant excerpt only) setMethod("likelihood", signature(spec="Specification", covs="vector", states="vector"), function(spec, covs, states) { #### setMethod("likelihood", signature(model="Model", path="matrix"), function(model, path) { This

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 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

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 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 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'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 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)

Looking at the html generated help pages for a package I'm working on, I noticed a couple of things that looked a little funny. I suspect they are general features of the html for R (I don't usually look at it). First is a problem of vertical alignment in tables. The first column consistently aligned vertically *below* the alignment line of the bottom line of the second column. This was

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.

These are two small items that caught my eye. I'm looking at the R 1.7.1 2003-06-16 pdf reference manual. There are several refences to terms.default (e.g., p. 711, 712) but no definitions of it. I'm guessing this means terms, but it's a little puzzling. Also, the desription of terms.object does not mention that the "variables" attribute is actually a list of

I'm seeing various things fail when I try to next braces inside \eqn. This source \eqn{{\bf\beta}_j}{b(j)} is the vector produces this error ---------------------------------------------- [4] ! Missing $ inserted. <inserted text> $ l.258 \eqn{{\bf\beta}_j}{\bf\beta}_ j{{b(j)} is the vector of coefficients fo... I've inserted a

Using R Version 1.6.2 (2003-01-10) I did R CMD check. The package I was checking includes an .Rd file with an example. My package test directory included a file foo-Ex.R whose first line was WARNING: ignoring environment value of R_HOME There are some problems with this: 1. R_HOME is not set. 2. The line is a syntax error, causing the test to fail. Furthermore, I got this warning message a

I have two questions about packaging up code. 1) Weave/tangle advisable? In the course of extending some C code already in S, I had to work out the underlying math. It seems to me useful to keep this information with the code, using Knuth's tangle/weave type tools. I know there is some support for this in R code, but my question is about the wisdow of doing this with C (or Fortran, or other

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