TheSavageSam wrote: > > I am wishing to write my own random distribution simulation function using > C programmin language(for speed) via R. I am familiar with R programming > but somewhat new to C programming. I was trying to understand "Writing R > extensions" -guide and its part 6.16, but I found it hard to > understand(http://cran.r-project.org/doc/manuals/R-exts.html#Standalone-Mathlib). > I also tried to get familiar with the example codes, but it didn't make me > any wiser. > > The biggest problem seems to be how to get(what code to write in C) random > uniform numbers using Rmath. That seems to be too complicated to me at the > moment. So if someone of you could give a small example and it would > definitely help me a lot. All I wish to do first is to finally write(and > understand) my own function similar to what you run in R Command line via > command "runif". > > And all this I am hoping to do without recompiling my whole R. Instead of > that I think that it is possible to use dyn.load("code.so") in R. (Yes, I > use linux) >
If the code in this message gets mangled by the mailing list, try viewing it on nabble: http://n4.nabble.com/Writing-own-simulation-function-in-C-td1580190.html#a1580190 ... Actually, funny story... I wrote this post using Nabble, but the Spam Detector refuses to let me post it claiming that there are "too many sex words" due some constructs in the R language. So I guess this confirms that using R is the quickest way to increase your statistical manhood/womanhood. As a result of this, all code chunks have been moved to a gist at GitHub-- links are provided. ... I would suggest first learning how to implement callbacks to R functions from C functions rather than diving straight into calling the C implementations of R functions. The reasons are: 1. You already know how the R functions is supposed to be called. 2. You don't have to search through the R source to track down the C implementation of the function you are after. I would also bet that the overhead of calling an R function that calls it's compiled implementation is not that significant-- but I haven't done any profiling. The way I would approach this problem would be to create a new package since the package structure helps manage the steps involved in loading and testing the compiled code. So, here's an example of calling the R function runif() from C (tested on Linux Mint 8 with R 2.10.1): Start an R terminal and execute the following: setwd( 'path/to/wherever/you/like/to/work' ) # Create a dummy wrapper function to start the package with myRunif <- function( n, min = 0, max = 1 ){} # Start the package package.skeleton( 'RtoC' ) Now, quit R. You'll notice that a folder called RtoC appeared in the directory where R was working. Edit RtoC/R/myRunif.R to contain the following: see: http://gist.github.com/323498#file_my_runif.r The above R function is a wrapper for our compiled C function. It states that we will be calling a C routine named "myRuinf" and passing the parameters n, min and max. The .Call() interface will pass these arguments as separate SEXP objects, which are the C-level representation of R objects. The alternatives to .Call are .External which gathers all the arguments up and passes them as a single SEXP and .C() which passes them directly as basic C variables (care must be used with .C(), you may need to wrap things in as.double(), as.integer(), etc.). I am using .Call() over .C() because .Call() allows an SEXP (i.e. native R object) to be returned directly. .External() also returns a SEXP, but we are calling back to an R function so we would have to split each argument into a separate SEXP anyway. With .C() we would have to coerce the C types to separate SEXP variables. Furthermore, with .C(), an additional variable to hold the return value would have to be included as an argument the function as we are expecting to return a vector and all the arguments are scalars. So, now the task is to write the C implementation of myRunif. Create the following folder in the package: mkdir RtoC/src Any C or Fortran code placed in the /src folder of an R package will be automagically compiled to a shared library that will be included when the package is installed. So, let's add a C routine, RtoC/src/myRunif.c: see: http://gist.github.com/323498#file_m_runif.c The first thing that myRunif.c does is to locate the namespace of the stats function. This is important as runif() lives in that namespace and we will be executing a call to it later. The first thing to note is that results returned by R are assigned inside of a PROTECT() statement. This is because SEXPs are *pointers* to data and we don't want the R garbage collecter to munch the data we're pointing to before we get a chance to use it. After finding the stats namespace, the second operation is to set up the call back to the R function ruinf()-- this is started by creating a vector of type "language" that has a length of 4-- one slot for the function name and then three more slots because runif takes three arguments. Next, the slots of the LANGSXP are filled- first by locating the name of the function we wish to call using findFun() and then by filling in the arguments. The assignment of each argument is followed by a call to SET_TAG which tags the argument with it's name in the R function. Basically, lines 18-29 construct an R function call of the form: runif( n = n, min = min, max = max ) Next, a SEXP is created to hold the results of the function call that was constructed. The variable is then assigned the results of the function call inside a PROTECT() Finally, UNPROTECT(3) is called before the results are returned to R. This is don because we called PROTECT() 3 times. If you don't call UNPROTECT(), you will see warnings about a "Stack Imbalance" when running your code in R. It is worth noting that I am being incredibly verbose with lines 15-36, I could have replaced them with: see: http://gist.github.com/323498#file_my_runif_concise.c In the case above, lang4() provides a shortcut for building a LANGSXP with 4 slots and the call arguments are matched by position. I showed the long version in case you want to execute a call with more than 3 arguments or you need to execute a call using key=value matching. Finally, you need to add one more routine that causes the dynamic library to be loaded when the R package is loaded. Since the package doesn't have a namespace, we'll use the function .First.lib() and place it in RtoC/R/zzz.R: see: http://gist.github.com/323498#file_zzz.r If you add a namespace to your package, you will need to use .onLoad() instead of .First.lib(). Now build and install the package: R CMD build RtoC R CMD INSTALL RtoC_0.1.tar.gz You should be able to execute the following in R: library( RtoC ) myRunif( 10 ) [1] 0.8072035 0.3978969 0.6492380 0.3614537 0.9150126 0.7287221 0.4242656 [8] 0.9401839 0.8275597 0.7270001 myRunif( 10, -10, 10 ) [1] 0.669904 -7.603356 -6.632470 6.753585 -9.424831 8.758971 -4.430569 [8] 3.737836 -5.297374 -9.968060 Happy hacking! -Charlie ----- Charlie Sharpsteen Undergraduate-- Environmental Resources Engineering Humboldt State University -- View this message in context: http://n4.nabble.com/Writing-own-simulation-function-in-C-tp1580190p1580423.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
