similar to: Rprof/UseMethod

Hello, (This is follow up from this thread: http://www.nabble.com/execution-time-of-.packages-td22304833.html but with a different focus) I am often confused by the result of the profiler, when a loop is involved. Consider these two scripts: script1: Rprof( ) x <- numeric( ) for( i in 1:10000){ x <- c( x, rnorm(10) ) } Rprof( NULL ) print( summaryRprof( ) ) script2:

Hi Consider the following example: f <- function(expr) g(expr) g <- function(expr) { ? h(expr) } h <- function(expr) { ? expr # evaluation happens here ? i(expr) } i <- function(expr) { ? expr # already evaluated, no costs here ? invisible() } rprof <- tempfile() Rprof(rprof) f(replicate(1e2, sample.int(1e4))) Rprof(NULL) cat(readLines(rprof), sep = "\n") #>

I'm picking up this 5-year old thread. 1. About the four memory allocations without a stacktrace I think the four memory allocations without a stacktrace reported by Rprofmem(): > Rprofmem(); x <- raw(2000); Rprofmem("") > cat(readLines("Rprofmem.out", n=5, warn=FALSE), sep="\n") 192 :360 :360 :1064 :2040 :"raw" are due to some

I noticed this oddity about R profiling and setMethod. First, I "test out" Rprof. > require(methods) Loading required package: methods [1] TRUE > > Rprof("test.out") > data.frame("a") X.a. 1 a > Rprof(NULL) So far, so good. Next, I define myClass. > setClass("myClass", representation(mySlot = "numeric")) [1]

The Rprofmem facility is currently enabled only if the configuration option --enable-memory-profiling is used. However, the overhead of having it enabled is negligible when profiling is not actually being done, and can easily be made even smaller. So I think it ought to be enabled all the time. I've attached a patch doing this, which also makes a number of other improvements to Rprofmem,

Hello R developers, Platform: Windows 2000. Compiler: GNU GCC - 2.95.2 (CRTDLL) I have a C function for evaluating commands using eval(SEXP,SEXP) which is included in a personal make of R.dll. (Similiar to R_Proxy_evaluate() in rproxy_impl.c) Using SETJMP to catch errors works but there is a problem. For example, the following code: if (SETJMP(R_ToplevelContext->cjmpbuf)) //

Full_Name: Jelle Goeman Version: 1.9.0 OS: mingw32, windows 2000 Submission from: (NULL) (145.88.209.33) Hi Everyone, I get very strange results using the function hasArg with the ... function argument. In my own function: > gt <- globaltest(X,Y) > sampling(gt) works fine, but > sampling(globaltest(X,Y)) results in: Error in eval(expr, envir, enclos) : "missing"

I'm in the very initial stage of expanding the formula processing in my quantile regression function rq() to handle additive nonparametric components, say qss(x), or qss(x,z). I need some advice about strategy for formula processing. My initial foray was to use: terms(formula,specials="qss") and then modify the components of the resulting terms.object. But in changing formula

I am having some difficulties using dynamically loaded C functions on a Sparc 10 with R compiled using cc (both R-0.49 and R-0.50). I get sporadic errors with the error message: Error: character variables must be duplicated in .C/.Fortran, with C functions which pass character variables. my C functions look like: anyfunc(char **filename, other variables ) This error message appears about 25%

I am having some difficulties using dynamically loaded C functions on a Sparc 10 with R compiled using cc (both R-0.49 and R-0.50). I get sporadic errors with the error message: Error: character variables must be duplicated in .C/.Fortran, with C functions which pass character variables. my C functions look like: anyfunc(char **filename, other variables ) This error message appears about 25%

Recursively walk the formula performing the replacement: g <- function(e, ...) { if (length(e) > 1) { if (identical(e[[2]], as.name(names(list(...))))) { e <- eval(e, list(...)) } if (length(e) > 1) for (i in 1:length(e)) e[[i]] <- Recall(e[[i]], ...) } e } g(f, lambdas = 2:3) ## y ~ qss(x, lambda = 2L) + qss(z, 3L) + s On Fri, Oct

Suppose I have a formula like this: f <- y ~ qss(x, lambda = lambdas[1]) + qss(z, lambdas[2]) + s I?d like a function, g(lambdas, f) that would take g(c(2,3), f) and produce the new formula: y ~ qss(x, lambda = 2) + qss(z, 3) + s For only two qss terms I have been using g <- function(lambdas, f){ F <- deparse(f) F <- gsub("lambdas\\[1\\]",lambdas[1],F) F

I'm confused by this: > identical(function() {}, function() {}) [1] FALSE Yet, after loading the Matrix package (which redefines det), the following is checked (in library.checkConflicts): > identical(get("det", baseenv()), get("det", asNamespace("Matrix")), ignore.environment=T) [1] TRUE I've looked at the code in identical.c and for closures it

Hi, Let me pick up this old thread. How does one extract the locations of the knots (ends of the segments) from the fit object below? Thanks, Vadim >From : roger koenker < roger_at_ysidro.econ.uiuc.edu > Date : Tue 31 May 2005 - 10:23:19 EST It is conventional to fit piecewise linear models by assuming Gaussian error and using least squares methods, but one can argue that

I'd like to mention that there is a new quantile regression package "nprq" on CRAN for additive nonparametric quantile regression estimation. Models are structured similarly to the gss package of Gu and the mgcv package of Wood. Formulae like y ~ qss(z1) + qss(z2) + X are interpreted as a partially linear model in the covariates of X, with nonparametric components defined as

coding style fixes. FIXME: check that compiled bin the same1!! --- usr/kinit/initrd.c | 3 ++- usr/kinit/kinit.c | 12 ++++-------- usr/kinit/kinit.h | 20 ++++++++++---------- usr/kinit/name_to_dev.c | 6 +++--- usr/kinit/nfsroot.c | 5 ++--- 5 files changed, 21 insertions(+), 25 deletions(-) diff --git a/usr/kinit/initrd.c b/usr/kinit/initrd.c index

I'm having difficulty with an environmental issue: I have an additive model fitting function with a typical call that looks like this: require(quantreg) n <- 100 x <- runif(n,0,10) y <- sin(x) + rnorm(n)/5 d <- data.frame(x,y) lam <- 2 f <- rqss(y ~ qss(x, lambda = lam), data = d) this is fine when invoked as is; x and y are found in d, and lam is found the

I'm a day-old R newbie (but a war-weary S veteran), with couple of first-day questions: In R 0.61, this code fails. Ops.test <- function(e1,e2) { e1 <- NextMethod(.Generic) e1 } x <- 4 class(x) <- "test" y <- x < 3 The error message is "Error in NextMethod(.Generic) : negative length vectors are not allowed.". I assume it is a bug.

From: Gene Cumm <gene.cumm at gmail.com> CORE/cache: add cache priority parameter; Passing in DX=0 forces the cache entry to stay at the least recently used spot. Any other value allows it to continue on previous behavior of most recently used position in the list. Change ldlinux.asm and extlinux.asm (the only 2 users of cache) to pass in DX=1. Push/Pop DX unless it's definitely safe

Greetings all, Recently ran into a seg fault the keeps reoccurring whenever R-java is used (I believe). Not sure if this is an R issue or an openjdk issue so I'm trying to cover all bases with this report. I tried downgrading R version from 3.4.0 to 3.3.3 with the same results. Also tried downgrading java version from 1.8 to 1.7 with no luck. Running on a fully updated CentOS 7 x86_64,