similar to: iterated lapply

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){ >

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)],

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 <- as.list(X) rval <- vector("list", length(X)) for(i in seq(along = X)) rval[i]

On 23/02/2015 3:57 PM, Daniel Kaschek wrote: > Hi everybody, > > with the following code I generate a list of functions. Each function > reflects a "condition". 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

> 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 <-

> On Feb 25, 2015, at 5:35 PM, Benjamin Tyner <btyner at gmail.com> wrote: > > Actually, it depends on the number of cores: Under current semantics, yes. Each 'stream' of function calls is lazily capturing the last value of `i` on that core. Under Luke's proposed semantics (IIUC), the result would be the same (2,4,6,8) for both parallel and serial execution. This is

Actually, it depends on the number of cores: > fun1 <- function(c){function(i){c*i}} > fun2 <- function(f) f(2) > sapply(mclapply(1:4, fun1, mc.cores=1L), fun2) [1] 8 8 8 8 > sapply(mclapply(1:4, fun1, mc.cores=2L), fun2) [1] 6 8 6 8 > sapply(mclapply(1:4, fun1, mc.cores=4L), fun2) [1] 2 4 6 8 > >/ On Feb 24, 2015, at 10:50 AM,

Hi, When I fit the mixed-effects model by lme, I got the convergence error code = 1 message = iteration limit reached without convergence (9). Even after I increase the iteration numbers, it still did not work. The only way I found to avoid the error message is to increase the number of iterations for the EM algorithm, which need to be at least 1100. But the weird thing is that the correlation

I tried to use lsode with precompiled C code instead of an R function defining the derivatives. No problem so far. However, now I need to implement an ODE that contains spline functions, i.e. the derivatives at given time points depend on the value of a spline function at this time point. What is an efficient way to implement this in precompiled C code? -- Daniel Kaschek <daniel.kaschek at

Below is an example of a problem I encounter repeatedly when I write functions. A call works at the command line, but it does not work inside a function, even when I have made sure that all required variables are available within the function. The only way I know to solve it is to make the required variable global, which of course is dangerous. What is the elegant or appropriate way to solve

Dear Martyn, On Fr, Jan 15, 2016 at 4:01 , Martyn Plummer <plummerm at iarc.fr> wrote: > > Alternatively, you may be able to control the maximum number of > threads > by setting and exporting an appropriate environment variable depending > on what backend you are using, e.g. OPENBLAS_NUM_THREADS or > MKL_NUM_THREADS. Thanks a lot. Running export OPENBLAS_NUM_THREADS

Hi Óscar, Thank you for your prompt reply. Unfortunately, I still need more guidance as using the Demo page to generate C++ code didn't result in a global variable being used. Basically, I'm following your advice to use a LoadInst: Value *v = new LoadInst(result, "", theBasicBlock); Function *myfn = cast<Function>(v); I was not sure how I could get a BasicBlock for the

Hi, I have the following hypothetical optimization problem: -det(t(x-A%*%x1)%*%(x-A%*%x1)) where A,x,x1 are matrices. A coefficients and x and x1 are variable matrices or vectors. I tried to apply optim and nlm functions but I kept receive the following massage: Error in A%*%x1 : non-conformable arguments. The massage appears even the -det() can be calculated and the dimensions are checked. here

I am trying to calculate monthly means by year of phosphates and nitrates from a multi year data set. Can anybody help me out with the most effective way to do this? My data looks like this: Collection_Date                   Test.Name              Value 2000-01-24 17:00:00            Phosphate               0.108 2000-01-24 17:00:00            Nitrate                     0.037 2001-11-12

I am trying to build a C language DLL and it works well with i386 but when I compile with it substituted by x64, like the FAQ page says, the result is an error message: C:/PROGRA~1/R/R-214~1.0/bin/x64/R.ddl: file not recognized : File format not recognized collect2: ld returned 1 exit status How can I solve this? The problem is with R 2-14.0 and 2-13.0 The complete source code is /* * myfun.c

Hi, I would like to write a function which has the following syntax: myfn <- function(formula, ftime, fstatus, data) { # step 1: obtain terms in `formula' from dataframe `data' # step 2: obtain ftime from `data' # step 3: obtain fstatus from `data' # step 4: do model estimation # step 5: return results } The user would call this function as: myfn(formula=myform,

Hi all, I need to test the significnce of difference between slopes of two regression lines and regression line with theoretical line. I try to use Slope.test function from emu package, but an error occured... library(emu) d1<-data.frame(P1=c(1,2,3,5,7,8,9,13,14,15), P2=c(1,2,5,8,11,13,15,15,18,24), R=c(2,7,8,9,16,21,27,31,33,36)) # First data set m1<-lm(R~P1+P2+P1*P2,data=d1) # Regr.

Hi All, I have a formula which has a factor with NAs in it. I wish to keep these in the model matrix, but the NA information is currently lost (the rows are kept but the NA gets converted to 0). Any ideas as to how I can keep NAs in? e.g. junk <-

On 9/13/2013 6:06 PM, Nick Kledzik wrote: > On Sep 13, 2013, at 3:35 PM, Shankar Easwaran <shankare at codeaurora.org> wrote: >> This would work only if an alias is another name for the same symbol(weak symbols). > I don’t know what that means. Can you clarify? Doesnt this imply that the alias atom is a zero sized atom ? >> If what is being aliased is another function

Sometimes one might like to obtain pointwise bootstrap bias-corrected, accelerated (BCA) confidence intervals for a large number of statistics computed from a single dataset. For instance, one might like to get (so as to plot graphically) bootstrap confidence bands for the fitted values in a regression model. (Example: Chiu S et al., Early Acceleration of Head Circumference in Children with