similar to: "rval, symbol print name too long"?

Anyone who can help me with the following question? How can I add weight to [x,y] coordinates on a graph/scatterplot? Background: Monte Carlo simulation generated 730,000 [x,y] coordinates with a weight attached (from 0-0.5). Both x and y are rounded and fit on a raster with x-axis 0-170 months (smalles unit = 1 month) and y-axis 0-6 (smallest unit=0.1). I would like every [x,y] to add its

Question: I created an array with 164 columns and 70 rows. I would like every column to have unit value 1 and the rows 0.1, 0.2, 0.3 up to 7. How can I define that? Thanks for your help! Good weekend! Akko _________________________________________________________________ [[alternative HTML version deleted]]

Is there anyone who can help me with the following? It's hard to describe in a few words, but hope you'll understand. I attempt to model refugee mortality over time using Monte Carlo. Before doing repetitive simulations, my function should go through only one survey/row at the time, but instead gives outcomes of all studies/rows, which is not correct, because it uses random numbers

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

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

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)

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

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)],

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

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"

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'

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

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

> 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 <-

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 <-

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

As far as I can tell, there's no built-in way to get capture.output() to capture messages (stderr) instead of stdout ... suggested, fairly trivial, patch below. f <- function() { message("abc") cat("def\n") } x <- capture.output(f()) ## prints 'abc' x ## value: "def" source("~/R/r-devel/src/library/utils/R/capture.output.R") x

I have the ewma function as shown below. I think I copied it from an oldSplus help page on filter and then modified it with a lot of help from Achim. ewma<-function(x,lambda = 1, init = x[1]) { rval<-filter(lambda*coredata(x),filter=(1-lambda),method="recursive",ini t=init) rval<-zoo(coredata(rval),index(x)) rval } It sort of works but , if there are NA values in the input