[R-pkg-devel] How to obtain intercept of intercept-only glm in Fortran?

[Wang, Zhu]

Ivan's answer is very impressive.   

Michael,

I am open to whatever suggestions but I am not aware a simple closed-form solution for my original question.

Thanks,

Zhu

> On May 11, 2019, at 8:01 AM, Michael Weylandt <michael.weylandt using gmail.com> wrote:
> 
> 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.r00t using gmail.com> wrote:
>> 
>> On Fri, 10 May 2019 16:17:42 +0000
>> "Wang, Zhu" <wangz1 using 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
>> 
>> ______________________________________________
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>> https://stat.ethz.ch/mailman/listinfo/r-package-devel
>> 
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