Hi,
Quick follow-up and additional question.
I got it working with fastglm:
#include <Rcpp.h>
using namespace Rcpp;
// [[Rcpp::export]]
NumericVector misclass(NumericMatrix obs_mat) {
// Obtain environment containing function
Rcpp::Environment base("package:stats");
// Obtaining namespace of fastglm package
Environment pkg = Environment::namespace_env("fastglm");
Function f = pkg["fastglm"];
...
mod_log = f(Named("x", ematrix), Named("y", d), Named("family",
"binomial"));
which works fine, but I cannot figure out how to get another link than
the default 'logit' (here for binomial()). I tried:
Named("family", "binomial(link = \"log\")")
Named("family", "binomial(link = log)")
Both gave me this error: "object of node 'function' not found.
Named("link", "log")
Named("make.link", "log")
These last two do not cause an error, but they give the default logit
link (as if the Named was not evaluated?).
Thanks,
Denis
Le 2024-09-04 16:24, Dirk Eddelbuettel a écrit :
On 4 September 2024 at 15:37, Denis Haine wrote:
| Hi,
|
| Sorry for a beginner's question. I'm trying to call an R function
(glm())
| inside my cpp code. The code compiles with no problem, but when I'm
running it,
| it cannot find the second element of the formula, i.e. the x in y~x.
The error
| message is: Error in eval (predvars; data, env) : object 'e' not
found.
|
| However, if I return 'e', it is correctly calculated. I guess the
formula is
| not correctly evaluated, but I haven't found any examples that could
point me
| in one direction or another.
|
| #include <Rcpp.h>
| using namespace Rcpp;
| // [[Rcpp::export]]
| NumericVector misclass(NumericMatrix obs_mat) {
| // Obtain environment containing function
| Rcpp::Environment base("package:stats");
|
| // Picking up glm() and summary() function from base stats
| Rcpp::Function glm_r = base["glm"];
| Rcpp::Function sum_r = base["summary.glm"];
You are running glm() from base here. At the speed of glm() from base.
That you call it from C++ does not make it faster, sadly, it just
creates
more work for you. Sorry to be a bearer of bad news.
| Rcpp::NumericVector d = obs_mat(_, 1);
| Rcpp::NumericVector e = no_init(n);
| Rcpp::NumericVector mod_coef = no_init(n);
| // e is calculated in other section of the code
| e = as<IntegerVector>(e);
| Rcpp::List mod_pois = glm_r(_["formula"] = "d ~ e",
| _["family"] = "poisson");
You need to 'work out' (ie expand) the formula on the R side. I have
done
that for the lm() case of model matrices and whatnot in the fastLm()
example(s) inside eg RcppArmadillo. Once you have done the R-level
unpacking
you can call the C++ function to fit.
Big takeaway from that exercise there: the time you spent dealing with
the
(convenient) formula dominates the gain from using a different,
simpler,
slightly faster 'fitter' by many orders of magnitude.
[ I think there are some packages dealing with glm() fits using Rcpp
and
friends among the by now over 2800 CRAN packages using Rcpp -- but I
can't
right now recall their names. You may find some help in those if you
find
them, and if my recollection was correct in the first place ... ]
Dirk
| Rcpp::List mod_sum = sum_r(mod_pois);
| Rcpp::NumericMatrix M_coef = mod_sum[12];
| mod_coef = M_coef(2, 1);
|
| return mod_coef;
| }
|
|
| I also tried providing the formula in the call, i.e. NumericVector
misclass
| (NumericMatrix obs_mat, Formula f) and using it in glm_r, i.e.
glm_r(_
| ["formula"] = f, etc. but with the same outcome.
|
| Thanks,
|
| Denis
|
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