similar to: pinning down symbol values (Scoping/Promises) question

Apparently Scheme is clever and can turn certain apparently recursive function calls into into non-recursive evaluations. Does R do anything like that? I could find no reference to it in the language manual. What I'm wondering is whether there are desirable ways to express recursion in R. Thanks. -- Ross Boylan wk: (415) 514-8146 185 Berry St #5700

Is there any good source of information on how S4 classes (and methods) work from C? E.g., for reading how to read a slot value how to invoke a method how to test if you have an s4 object For writing, how to make a new instance of an S4 object. I've found scattered hints in the archive, including a link to a talk on this subject "I am using C code to create an S4 object based on

Is there a way to control the aspect ratio of plots using ggplot? Specifically, I'm using the formula=a~b argument to produce a grid of plots, but the overall width of the result seems to vary for reasons that are obscure to me. This affects not only the appearance of the plots but the amount of space available for the title (which seems to be right justified relative to the right edge of the

I'm invoking R from withing a shell script like this R --no-save --no-restore --gui=none > `hostname` 2>&1 <<BYE # various commands here BYE I would like to regain control from the invoking terminal at some point. I tried source(stdin()) but got a syntax error, presumably stdin is the little shell here snippet (the part between <<BYE and BYE). Is there some way to

I'd like to evaluate a function at each point on a 2 or 3-D grid. Is there some function that already does this, or generates the grid of points? My search has led me to the grid and lattice packages, and I found a reference to the sp package (e.g., SpatialGrid) for this. There are things in there that might be relevant, but at first blush many of them are embedded in other concepts (grobs,

I have some C data I want to pass back to R opaquely, and then back to C. I understand external pointers are the way to do so. I'm trying to find how they interact with garbage collection and object lifetime, and what I need to do so that the memory lives until the calling R process ends. Could anyone give me some pointers? I haven't found much documentation. An earlier message

I've been looking at documenting S4 classes and methods, though I have a feeling many of these issues apply to S3 as well. My impression is that the documentation system requires or recommends creating basically the same information in several places. I'd like to explain that, see if I'm correct, and suggest that a more automated framework might make life easier. PROBLEM Consider a

I have some calculations that require persistent state. For example, they retain most of the data across calls with different parameters. They retain parameters across calls with different subsets of the cases (this is for distributed computation). They retain early analysis of the problem to speed later computations. I've created an S4 object, and the stylized code looks like this calc

I would like to be able to trace execution into calls below the current function, or to follow execution as calls return. This is roughly the distinction between "step" and "next" in many debuggers. I would also like to be able to switch to a location further up the call stack than the location at which I enter the debugger, to see the context of the current operations. Are

Writing R Extensions mentions that a package developer can provide a Makefile, but gives very little information about what should be in it. It says there must be a clean target, and later on there's mention of $(SHLIB): $(OBJECTS) $(SHLIB_LINK) -o $@ $(OBJECTS) $(ALL_LIBS) (in the F95 discussion). What should a Makefile provide, and what can it assume? In other words,

I have a small S4 class for which I've written a page grouping many of the accessors and replacement functions together. I would be interested in people comments on the approach I've taken. The code has a couple of decisions for which I could imagine alternatives. First, even simple get/set operations on class elements are wrapped in functions. I suppose I could just use myinstance at

I would like to checkpoint some of my calculations in R, specifically those using optim. As far as I can tell, R doesn't have this facility, and there seems to have been little discussion of it. checkpointing is saving enough of the current state so that work can resume where things were left off if, to take my own example, the system crashes after 8 days of calculation. My thought is that

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

During R CMD check I get this: ** building package indices ... Error in eval(expr, envir, enclos) : could not find function "readingError" Execution halted ERROR: installing package indices failed The check aborts there. readingError is a function I just added; for reference setClass("readingError", contains="matrix") readingError <- function(...)

I have a data frame "reading" that includes a logical variable "OLT" along with response variable "Reading" and predictor "True" (BOTH are numeric variables; it's "True" as in the true value). When I suppress the intercept, model.matrix gives me OLTTRUE and OLTFALSE columns. Why? Can I do anything to prevent it? > r <-

Has anybody tried to use Rmpi with the OpenMPI library instead of LAM/MPI? LAM appears to be somewhat hardcoded in the Rmpi setup. Before I start to experiment with changing this, has anybody else tried Rmpi with non-LAM MPI implementations? Dirk -- Hell, there are no rules here - we're trying to accomplish something. -- Thomas A. Edison

I have a single data file inputs.RData that contains 3 objects. I generated an Rd page for each object using prompt(). When I run R CMD check I get * checking for code/documentation mismatches ... WARNING Warning in utils::data(list = al, envir = data_env) : data set 'gold' not found (gold is one of the objects). This appears to be coming from the codocData function defined in

I've made a package for which R CMD build isn't producing very satisfactory results. I'll get to the details in a moment. I wonder if it would make sense to have my own makefiles (which already exist and are doing quite a lot) produce the .tar.gz file ordinarily produced by R CMD build. As far as I can tell, R CMD build basically tars up of the project directory after running some

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

1. promptClass generated a file that included \section{Methods}{ No methods defined with class "mspathDistributedCalculator" in the signature. } Yet there are such methods. Is this a not-working yet feature, or is something funny going on (maybe I have definitions in the library and in the global workspace...)? 2. Is the \code{\link{myS4class-class}} the proper way to cross-reference a