similar to: Variable selection in R

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Hi, I've been learning how to use R TclTk in Windows over the last few months. I have recently put together a collection of examples of some common widgets and their corresponding R code, at http://bioinf.wehi.edu.au/~wettenhall/RTclTkExamples/ I would be interested in any feedback - Is it useful? Does it contain any significant errors or bad coding style? Does anyone else want to

Thanks Peter, I've started a new R session (in Windows) and managed to get both ways working now : ### THIS WORKS !!! ### library(tcltk) tclRequire("IWidgets") tt<-tktoplevel() combo <- tkwidget(tt,"iwidgets::combobox") tkpack(combo) ### AND THIS WORKS TOO !!! ### tt<-tktoplevel() win <- .Tk.subwin(tt)

Hi, I am trying to create a drop-down combobox in R TclTk. The following works fine for a ListBox but fails for a combobox: ################# THIS WORKS FINE - CREATES AN EMPTY LISTBOX ## tt<-tktoplevel() win <- .Tk.subwin(tt) .Tcl(paste("listbox",.Tk.ID(win),.Tcl.args())) tkpack(win) ################## THIS FAILS - ATTEMPTS TO CREATE A COMBOBOX ## tt<-tktoplevel() win

Hi, I have successfully installed R 1.7.0 and ActiveTcl 8.4.2.0 in Windows 2000. Yes, I know that Tcl is already bundled with R 1.7.0, but I want to use the iwidgets package to create a drop-down listbox, and I don't think iwidgets is included in the bundled R 1.7.0/TclTk installation package. I have set the environment variables TCL_LIBRARY and MY_TCLTK to the directory where I

This is just a suggestion/wish that it would be nice for the F-distribution functions to recognize limiting cases for infinite degrees of freedom, as the t-distribution functions already do. The t-distribution functions recognize that df=Inf is equivalent to the standard normal distribution: > pt(1,df=Inf) [1] 0.8413447 > pnorm(1) [1] 0.8413447 On the other hand, pf() will accept Inf

In R 2.1.1, the default behaviour of scan() was changed to process all C-style escapes, even when a delimiter was specified using the 'sep' argument. A new argument 'allowEscapes' was introduced to turn this processing off. Because read.table() calls scan(), read.table() inherits the new default behaviour of scan() but without a way to turn it off. For example, reading a file

Martin Maechler has suggested that I post this comment to r-devel. It was originally posted to bioconductor. --------------------------------- I'd like to raise the issue of a capitalization convention for naming objects in R. Almost everything in R used to be lowercase but recently there is increasing use of mixed upper/lower case to define names. There is potential for using the

>[Rd] boxplot() defaults {was "boxplot in extreme cases"} >Martin Maechler maechler at stat.math.ethz.ch >Mon Nov 8 10:36:42 CET 2004 > > AndyL> Try: > > AndyL> x <- list(x1=rep(c(0,1,2),c(10,20,40)), > x2=rep(c(0,1,2),c(10,40,20))) > AndyL> boxplot(x, pars=list(medpch=20, medcex=3)) > > AndyL> (Cf ?bxp, pointed to from

Should R CMD check consider it an error to define a new method using setMethod for a class which is not currently defined? It seems to me that it would be best not to consider this an error. What currently happens in that setMethod issues a warning, quite correctly. This warning produces an object 'last.warning'. Then R CMD check issues a WARNING that 'last.warning' is an

I'm writing a load of output to a file using cat(...), but I can't work out how to turn off scientific notation for small numbers. When I originally tried this a while a go I ended up building up a string variable, which is rather clumsy... Many thanks, David -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

I've noticed a curious interpretation of escaped characters in \examples{} in .Rd files. For example, if I type files <- dir(pattern="\\.txt") at the R prompt, I will get a vector containing all file names in the current directory containing the string ".txt". If I put \examples{ files <- dir(pattern="\\.txt") } in an .Rd file of a package,

Dear all, I am trying to write a package using formal methods and classes from the methods package. I have not been able to get the package to pass rcmd check without warnings, because rcmd check does not recognize my generic functions as code objects and therefore queries why they have documentation entries. I have isolated the problem in a very small trivial example which I give below. I

I am another person who has had trouble documenting S4 classes and (particularly) methods. The methods package itself is pretty cool by the way, but it is a pity that there are as yet no guidelines on S4 in the "Writing R Extensions" document. I have actually put together a guide on S4 documentation myself for the use of my own lab which is at

Dear all, I'm writing to ask for advice as to best practice. A PhD student working with me is writing C++ code that we hope to make public as src code in our Bioconductor package edgeR. He wants to call the function fmm_spline, which is part of the source code for the stats package http://svn.r-project.org/R/trunk/src/library/stats/src/splines.c, from his C++ code. This function

Hi Ivan, An apology, I was away for quite a bit. To reproduce the setup: I have been using the default GCC in RHEL 9.1. gcc -v Using built-in specs. COLLECT_GCC=gcc COLLECT_LTO_WRAPPER=/usr/libexec/gcc/x86_64-redhat-linux/11/lto-wrapper OFFLOAD_TARGET_NAMES=nvptx-none OFFLOAD_TARGET_DEFAULT=1 Target: x86_64-redhat-linux Configured with: ../configure --enable-bootstrap --enable-host-pie

On Tue, Apr 28, 2020 at 9:00 PM Shian Su <su.s at wehi.edu.au> wrote: > > Thanks Simon, > > I will take note of the sensible default for core usage. I?m trying to achieve small scale parallelism, where tasks take 1-5 seconds and make fuller use of consumer hardware. Its not a HPC-worthy computation but even laptops these days come with 4 cores and I don?t see a reason to not make

Do NOT use mcparallel() in packages except as a non-default option that user can set for the reasons Henrik explained. Multicore is intended for HPC applications that need to use many cores for computing-heavy jobs, but it does not play well with RStudio and more importantly you don't know the resource available so only the user can tell you when it's safe to use. Multi-core machines are

Hi, a few comments below. First, from my experience and troubleshooting similar reports from others, a returned NULL from parallel::mclapply() is often because the corresponding child process crashed/died. However, when this happens you should see a warning, e.g. > y <- parallel::mclapply(1:2, FUN = function(x) if (x == 2) quit("no") else x) Warning message: In

Thanks Henrik, That clears things up significantly. I did see the warning but failed to include it my initial email. It sounds like an RStudio issue, and it seems like that it?s quite intrinsic to how forks interact with RStudio. Given this code is eventually going to be a part of a package, should I expect it to fail mysteriously in RStudio for my users? Is the best solution here to migrate all