similar to: R CMD BATCH w/o inputs?

Dear all, I'm a newbie in R. I am running simulations using a fixed bandwidth in nonparametric regressions, sending all the output to a file myoutput.txt using sink("myoutput.txt"), & R is printing all warnings by the end of the simulation on the file. I know the source of the problem & can take care of it. However, finding a 50 MB file (where all the output is printed, e.g.

Dear all, The basename() function returns the extension also: > myfile <- "path1/path2/myoutput.txt" > basename(myfile) [1] "myoutput.txt" Is there any other function where it just returns plain base: "myoutput" i.e. without 'txt' - Gundala Viswanath Jakarta - Indonesia

Dear R wizards: is there a clean way to assign to elements in a list? what I would like to do, in pseudo R+perl notation is f <- function(a,b) list(a+b,a-b) (c,d) <- f(1,2) and have c be assigned 1+2 and d be assigned 1-2. right now, I use the clunky x <- f(1,2) c <- x[[1]] d <- x[[2]] rm(x) which seems awful. is there a nicer syntax? regards, /iaw ---- Ivo Welch

dear R wizards: is it possible to instruct R to save or no-save from inside R? or does this have to be given at invokation on the command-line? The same question applies to "--no-restore-data", although this presumably would have to be decided in a .First() function or something like it. on a similar note, I would love a CMD BATCH invokation to output just one line to stderr at the

Hello, I tried to call an external function of R from the following code in C++: void prodgdot(double *x, double *y, int *n, double *output) { int i; *output=0; for (i=0;i<*n;i++) { *output+=x[i]*y[i]; } } I compiled it using from my working directory in linux terminal and I think it's ok: giba at giba-desktop:~/mysrc/meus_testes_iniciais$ R CMD SHLIB

dear R experts--- I am experimenting with multicore processing, so far with pretty disappointing results. Here is my simple example: A <- 100000 randvalues <- abs(rnorm(A)) minfn <- function( x, i ) { log(abs(x))+x^3+i/A+randvalues[i] } ?## an arbitrary function ARGV <- commandArgs(trailingOnly=TRUE) if (ARGV[1] == "do-onecore") { ?library(foreach) ?discard <-

dear r-experts: there is probably a very easy way to do it, but it eludes me right now. I have a large data frame with, say, 26 columns named "a" through "z". I would like to define "sets of regressors" from this data frame. something like myregressors=c("b", "j", "x") lm( l ~ myregressors, data=... ) is the best way to create new

dear R wizards:? I am wrestling with reshape.? I have a long data set that I want to convert into a wide data set, in which rows are firms and columns are years. > summary(rin) firm fyear sim1 Min. :1004.00 Min. :1964.0 Min. : -1.00000 1st Qu.:1010.00 1st Qu.:1979.0 1st Qu.: -0.14334 Median :1016.00 Median :1986.0 Median : 0.00116 Mean

dear R experts---I am using the coef() function to pick off the coefficients from an lm() object. alas, I also need the standard errors and I need them fast. I know I can do a "summary()" on the object and pick them off this way, but this computes other stuff I do not need. Or, I can compute (X' X)^(-1) s^2 myself. Has someone written a fast se() function? incidentally, I think

this is not an important question, but I wonder why lm returns an error, and whether this can be shut off. it would seem to me that returning NA's would make more sense in some cases---after all, the problem is clearly that coefficients cannot be computed. I know that I can trap the lm.fit() error---although I have always found this to be quite inconvenient---and this is easy if I have only

Dear R wizards: I am trying (finally) to build a function that might be useful to others. In particular, I want to create a summary.lme (extended lm) method that [a] adds normalized coefficients and [b] white heteroskedasticity adjusted se's and T's. I believe I already know how to do the programming to do these two, at least in simple unweighted cases. Now my challenges are just [1]

dear R experts---now I have a case where I want to estimate very large regression models with many fixed effects---not just the mean type, but cross-fixed effects---years, months, locations, firms. Many millions of observations, a few thousand variables (most of these variables are interaction fixed effects). could someone please point me to packages, if any, that would help me estimate such

Dear R experts--- after many years, I am planning to give in and write my first R package. I want to combine my collection of collected useful utility routines. as my guide, I am planning to use Friedrich Leisch's "Creating R Packages: A Tutorial" from Sep 2009. Is there a newer or better tutorial? this one is 4 years old. I also plan on one change---given that the

dear R wizards: here is the strange question for the day. It seems to me that nrow() is very slow. Let me explain what I mean: ds= data.frame( NA, x=rnorm(10000) ) ## a sample data set > system.time( { for (i in 1:10000) NA } ) ## doing nothing takes virtually no time user system elapsed 0.000 0.000 0.001 ## this is something that should take time; we need to add 10,000

As an end-user, I wonder about Revolution R. Is the relationship between Revolution R and the R community at-large a positive one? Do the former contribute to the development efforts of the latter? Is there a competitive aspect? is their forum competitive with r-help? any other thoughts? (most of all, I simply hope that they help some of the many helpful experts on this forum, who have

Dear R Wizards: Running R 1.9.1. on amd64. Promise<- c(0,20,40); Expect<- c(0, 20, 0.2*20+.8*40 ); # this omits printing numbers on the axes labels. pdf(file = "bug.pdf" ); plot(Promise, Expect, type="b", ylim=c(0,60)); dev.off(); # this works postscript(file = "bug.eps" ); plot(Promise, Expect, type="b", ylim=c(0,60)); dev.off(); apologies if

dear R experts: does R have "static" variables that are local to functions? I know that they are usually better avoided (although they are better than globals). However, I would like to have a function print how often it was invoked when it is invoked, or at least print its name only once to STDOUT when it is invoked many times. possible without <<- ? sincerely, /iaw

dear R experts: I am trying to plot an empirical likelihood function in 3d. The values are not over a regular grid---I just searched the likelihood function to find the optimal value, and then computed a few values around it. (each point in the likelihood function takes a very long time to compute.) the likelihood values now sit in a csv file that has three columns, "mu",

dear R wizards: an operation I execute often is the deletion of all observations (in a matrix or data set) that have at least one NA. (I now need this operation for kde2d, because its internal quantile call complains; could this be considered a buglet?) usually, my data sets are small enough for speed not to matter, and there I do not care whether my method is pretty inefficient (ok, I

dear wizards: my .Rprofile has just one command for testing, loadhistory("~/.Rhistory") but this gives me an error on R startup: Error: couldn't find function "loadhistory" Invoking loadhistory() as the first interactive command works fine; incidentally, I believe loadhistory() in the .Rprofile worked in earlier or other platform R releases, too. Is the .Rprofile