Hi,
Let's say one has an object with multiple classes, and a generic function to
apply to it has associated S3 methods for more than one of those classes.
Further, the method it chooses (I do not know how; some order in the class
vector?) is not the suitable one and it produces an error. Would
Dear Iago,
The R S3 object system works as expected here, using the first available method
processing the class vector from left to right. The problem is that the broom
package doesn't export the confint.geeglm() method but rather reserves it for
internal use. I can't think why the package auth
On 17/06/2019 3:56 a.m., IAGO GINÉ VÁZQUEZ wrote:
Hi,
Let's say one has an object with multiple classes, and a generic function to
apply to it has associated S3 methods for more than one of those classes.
Further, the method it chooses (I do not know how; some order in the class
vector?) is
I had added a vignette to the coxme package and all worked well locally, but it
failed at
CRAN. The issue is that the vignette involves using coxme for pedigree
data, it
doesn't work without the kinship2 package, and I hadn't put in the necessary
"if
(require(" logic.
The question is
On Tue, 18 Jun 2019 at 19:03, Therneau, Terry M., Ph.D. via R-devel
wrote:
>
> I had added a vignette to the coxme package and all worked well locally, but
> it failed at
> CRAN. The issue is that the vignette involves using coxme for pedigree
> data, it
> doesn't work without the kinship2 p
Hi,
I'm looking for a most efficient way to call an R function from C++ in a
package. I know there are two functions (`R_forceAndCall` and `Rf_eval`)
that can do the "call" part, but both are slow compared to calling the same
function in R. I also try to use Rcpp and it is the worse one. Here is m
Hi Jiefei,
Calling into R from C++ code is more complicated than one might think.
Please see Tomas Kalibera's post here:
https://developer.r-project.org/Blog/public/2019/03/28/use-of-c---in-packages/index.html
The Rcpp Function class is more expensive than a regular Rf_eval()
because it tries to
For reference, your benchmark using UNWIND_PROTECT:
> system.time(test(testFunc, evn$x))
user system elapsed
0.331 0.000 0.331
> system.time(test(C_test1, testFunc, evn$x))
user system elapsed
2.029 0.000 2.036
> system.time(test(C_test2, expr, evn))
user system elapsed
2
On Tue, 18 Jun 2019 at 19:41, King Jiefei wrote:
>
> [...]
>
> It is clear to see that calling an R function in R is the fast one, it is
> about 5X faster than ` R_forceAndCall ` and ` Rf_eval`. the latter two
> functions have a similar performance and using Rcpp is the worst one. Is it
> expected
Hello Kevin and Iñaki,
Thanks for your quick responses. I sincerely appreciate them! I can see how
complicated it is to interact with R in C. Iñaki's suggestion is very
helpful, I saw there is a lot of performance gain by turning the flag on,
but sadly the best performance it can offer still canno
In specific cases fligner.test() can produce a small p-value even when both
groups have constant variance.
Here is an illustration:
fligner.test(c(1,1,2,2), c("a","a","b","b"))
# p-value = NA
But:
fligner.test(c(1,1,1,2,2,2), c("a","a","a","b","b","b"))
# p-value < 2.2e-16
This c
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