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Running R.app on Mac OS X 10.4 > version _ platform powerpc-apple-darwin8.6.0 arch powerpc os darwin8.6.0 system powerpc, darwin8.6.0 status major 2 minor 3.1 year 2006

Running R.app on Mac OS X 10.4 > version _ platform powerpc-apple-darwin8.6.0 arch powerpc os darwin8.6.0 system powerpc, darwin8.6.0 status major 2 minor 3.1 year 2006

Dear useRs and wizaRds, I am currently developing a set of functions using S4 classes. On the way I encountered the problem exemplified with the code below. For some reason the 'validity' method does not seem to work, i.e. does not check for errors in the specification of the slots of the defined class. Any hints? My understanding of the whole S4 system was that validity checks are made

...sValue *rval) { SwfdecNetConnection *conn = SWFDEC_NET_CONNECTION (obj); const char *url; diff --git a/libswfdec/swfdec_player_as.c b/libswfdec/swfdec_player_as.c index 9c1ec35..e983c6f 100644 --- a/libswfdec/swfdec_player_as.c +++ b/libswfdec/swfdec_player_as.c @@ -30,10 +30,10 @@ /*** INTERVALS ***/ static void -swfdec_player_do_set_interval (gboolean repeat, SwfdecAsObject *obj, guint argc, +swfdec_player_do_set_interval (gboolean repeat, SwfdecAsContext *cx, guint argc, SwfdecAsValue *argv, SwfdecAsValue *rval) { - SwfdecPlayer *player = SWFDEC_PLAYER (obj->context); + S...

This is a simple SSA code generation 101 question. If I follow the IR code generation techniques in the Dragon book the statement x = y + z would translate into something like this in SSA/LLVM %0 = add %y, %z %x = %0 Obviously "copy instructions" like %foo = %bar are senseless in SSA since %foo and %bar are immutably fixed to the same value and there is no need for two aliases

I installed the locfit package under Linux (gcc 2.7.2). Installation was ok but > x <- runif(200) > y.compl <- 10*x*x*rgamma(200,3) > med.y <- median(y.compl) > cens <- ifelse(y.compl<=med.y,1,0) > y <- cens * y.compl + (1-cens)*med.y > library(locfit) > m <- locfit(y~x,cens=cens,family="gamma") /usr/local/src/R-0.61.1/bin/R.binary: can't

I'd like to learn the process of revising R functions & packages and then submitting proposed patches to the R Core team. Would someone be willing to mentor me through one example? For starters, consider an example. I'd like to revise the t.test function to return the stderr value to the user. We only need to change the "rval" in the third-from-the end line of

Would introducing the new frame, with the call to local(), cause problems when you use frame counting instead of <<- to modify variables outside the scope of lapply's FUN, I think the frame counts may have to change. E.g., here is code from actuar::simul() that might be affected: x <- unlist(lapply(nodes[[i]], seq)) lapply(nodes[(i + 1):(nlevels - 1)],

I have an application where I need to preserve object attributes across calls to 'lapply'. The current definition is: lapply <- function (X, FUN, ...) { FUN <- match.fun(FUN) if (!is.list(X)) X <- as.list(X) rval <- .Internal(lapply(X, FUN)) names(rval) <- names(X) return(rval) } Would it make sense to replace names(rval) <- names(X)

Hi! I've noticed the ecdf() R code (R ver. 2.5.0) contains two call to sort: --- [R-code] --- ecdf <- function(x) x <- sort(x) n <- length(x) if (n < 1) stop("'x' must have 1 or more non-missing values") vals <- sort(unique(x)) rval <- approxfun(vals, cumsum(tabulate(match(x, vals)))/n, method

It is my understanding, the alloca memory routines are used for forcing variables to be allocated on the stack frame -- which you would want for source level debugging. When SSA registers are used, LLVM will decide what goes into registers and what will spill over to the stack frame. I want the latter. --w Wayne O. Cochran Assistant Professor Computer Science wcochran at vancouver.wsu.edu

On Fri, Apr 22, 2011 at 10:40 AM, Wayne Cochran <wcochran at vancouver.wsu.edu> wrote: > This is a simple SSA code generation 101 question. > > If I follow the IR code generation techniques in the Dragon book the > statement >  x = y + z > would translate into something like this in SSA/LLVM >  %0 = add %y, %z >  %x = %0 > Obviously "copy instructions"

> On Feb 24, 2015, at 10:50 AM, <luke-tierney at uiowa.edu> wrote: > > The documentation is not specific enough on the indented semantics in > this situation to consider this a bug. The original R-level > implementation of lapply was > > lapply <- function(X, FUN, ...) { > FUN <- match.fun(FUN) > if (!is.list(X)) > X <-

I am trying to figure out why 'biglm' can handle large data set... According to the R document - "biglm creates a linear model object that uses only p^2 memory for p variables. It can be updated with more data using update. This allows linear regression on data sets larger than memory." After reading the source code below? I still could not figure out how 'update'

I am using an example posted in this help forum to work with a file. the head of the file looks like: 988887 2007-03-05 2007-06-01 90 3 5.450 205500.00 999.00 999.000 0.000 0 0 988887 2007-03-06 2007-06-01 90 3 5.450 205500.00 999.00 999.000 0.000 1 0 988887 2007-03-07 2007-06-01 90 3 5.450 205500.00 999.00 999.000 -0.100 2 0 988887 2007-03-08 2007-06-01 90 3 5.450 205500.00 999.00 999.000 -0.100

From: Daniel Kaschek <daniel.kaschek at physik.uni-freiburg.de> > ... When I evaluate this list of functions by > another lapply/sapply, I get an unexpected result: all values coincide. > However, when I uncomment the print(), it works as expected. Is this a > bug or a feature? > > conditions <- 1:4 > test <- lapply(conditions, function(mycondition){ >

Full_Name: Gregory R. Warnes Version: 1.2.0, 1.2.3 OS: SunOS gsun124 5.8 Generic_108528-03 sun4u sparc SUNW,Ultra-5_10 Submission from: (NULL) (12.18.36.49) When using expand.model.frame on a model that specifies a subset selection, an error is generated on the variable used for the subset selection. Example: > data <- data.frame(x=1:10,y=1:10,z=1:10,m=1:10) > model <- lm( y ~

Hello, I'm running R 2.14.1 on OS X (x86_64-apple-darwin9.8.0/x86_64 (64-bit)), with version 3.28 of Thomas Lumley's survey package. I was using predict() from svyglm(). E.g.: data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) out <- svyglm(sch.wide~ell+mobility, design=dstrat, family=quasibinomial()) pred.df <-

libswfdec/swfdec_as_context.c | 29 ++-- libswfdec/swfdec_as_context.h | 5 libswfdec/swfdec_as_frame.c | 47 +++++- libswfdec/swfdec_as_frame.h | 7 - libswfdec/swfdec_as_function.c | 12 + libswfdec/swfdec_as_function.h | 4 libswfdec/swfdec_as_interpret.c | 270 +++++++++++++++++++--------------------- libswfdec/swfdec_as_object.c | 31 ++++

The mem2reg pass will rewrite allocas and loads and stores to SSA virtual registers. Essentially it's a transformation from non-SSA to SSA form. That said, I don't know if you want your students to implement their own SSA transformation. Reid On Sat, Apr 23, 2011 at 1:57 PM, Cochran, Wayne Owen <wcochran at vancouver.wsu.edu> wrote: > It is my understanding, the alloca memory