Nevertheless, since I have just copied code from elsewhere, I have no idea what I'm doing! I have background in statistics and R programming, but parallelization is something I'm just starting to learn. I have attempted to do so by creating two separate clusters in an R session, and by comparing various options with microbenchmark(), it seems like I have achieved my goal. My question: to get the maximum speed, can I parallelize model fitting and dredging at the same time? In other words, can I combine/stack/add together the speed benefits of parallelizing glmmTMB() and dredge()? I know that both functions can be parallelized: glmmTMB() with the control argument, and dredge() with the cluster argument. My aim is to speed up as much as possible the dredge() function when applied to glmmTMB() models.
0 kommentar(er)
