This is very cool, but I wonder if it isn't over-kill for the larger problem.
In general, calculating the coefficient of an intercept-only GLM is just calculating (a transformation of) the MLE of a univariate exponential family distribution. (Things may be a bit trickier if the GLM also involves weights and offsets, not just an intercept, but I'm assuming it doesn't.) OP: Can you clarify why you want to invoke R's entire GLM machinery as opposed to just using the closed form solutions? Michael On Sat, May 11, 2019 at 7:23 AM Ivan Krylov <krylov.r...@gmail.com> wrote: > On Fri, 10 May 2019 16:17:42 +0000 > "Wang, Zhu" <wan...@uthscsa.edu> wrote: > > > Are there any examples or links for me to follow through more closely? > > Calling R functions from C++ is described at > <http://dirk.eddelbuettel.com/code/rcpp/Rcpp-quickref.pdf> and > elsewhere in Rcpp documentation. An example follows: > > --------------8<--------------glmfit.cpp--------------8<-------------- > #include <algorithm> > #include <Rcpp.h> > using namespace Rcpp; > > extern "C" double intercept_glm(size_t n, const double * response) { > // access functions from default environment > Function glm_fit("glm.fit"), coef("coef"); > > // intercept-only model: response ~ 1 > NumericVector x(n); > x.fill(1); > > // I couldn't find a way to wrap a double* into a NumericVector > // without copying anything, sorry; perhaps someone else > // can offer a solution > NumericVector y(n); > std::copy_n(response, n, y.begin()); > > // call the R function, convert the result back > return as<double>(coef(glm_fit(x, y))); > } > --------------8<--------------glmfit.cpp--------------8<-------------- > > Since this function is extern "C" and uses only primitive C types, it > should be fairly easy to call from Fortran. (C is the lingua franca of > programming languages). Fortran-C interoperability is well described in > "Modern Fortran Explained" by Metcalf et al. Here is the Fortran side > of the code: > > --------------8<--------------callglm.f90--------------8<-------------- > subroutine callglm(ret) > use, intrinsic :: iso_c_binding, only: c_size_t, c_double > ! using iso_c_binding here > ! - to get correct type of ret when R calls the function > ! - to convert variables before calling C function > implicit none > ! using F77-style arguments to match expectations of .Fortran() > real(c_double), intent(out) :: ret > ! toy data to compare against R code later > real :: y(10) = [10, 11, 20, 9, 10, 8, 11, 45, 2, 3] > ! the interface block declares an extern "C" function > interface > ! double intercept_glm(size_t n, const double * response) > function intercept_glm(n, response) bind(c) > use, intrinsic :: iso_c_binding > real(c_double) :: intercept_glm > integer(c_size_t), value :: n > real(c_double) :: response(*) > end function > end interface > > ! call the function as you would call any other function > ret = intercept_glm(int(size(y), c_size_t), real(y, c_double)) > end subroutine > --------------8<--------------callglm.f90--------------8<-------------- > > For a quick test, make sure that you have Rcpp installed and run: > > # adjust R version and path if your library is elsewhere > PKG_CPPFLAGS='-g -I ~/R/x86_64-pc-linux-gnu-library/3.3/Rcpp/include' \ > R CMD SHLIB callglm.f90 glmfit.cpp > R > library(Rcpp) > dyn.load('callglm.so') # change extension if needed > .Fortran('callglm', ret=numeric(1)) > # $ret > # [1] 12.9 > coef(glm.fit(rep(1, 10), c(10, 11, 20, 9, 10, 8, 11, 45, 2, 3))) > # [1] 12.9 > > To use this in a package, place both files in the src/ subdirectory of > your package and add LinkingTo: Rcpp in the DESCRIPTION. > > -- > Best regards, > Ivan > > ______________________________________________ > R-package-devel@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-package-devel > [[alternative HTML version deleted]] ______________________________________________ R-package-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-package-devel