![]() %CPU >90%), and thus will be near the top of the list (which will be in decreasing order). ![]() Look through the resulting list of PIDs (process IDs, which are unique identifiers for each process) and info for the R process, which will likely be using a great deal of CPU (%CPU eg. the command line.įirst do the following at the command line to obtain a list of processes including R: ps aux | grep R Specifically, I’ll show you how to do this from mac Terminal, i.e. Today, I’m doing a short post to show you how to get out of this situation by killing the process in R from outside the R environment. R can “hang” for these and many other reasons. Or, the function you’re using might require a maximum number of iterations to be specified, or else it will use an exhaustive search. Alternatively, there might be an issue with FORTRAN coding. by default) run a function that needs to visit the total number of models possible for your dataset or a certain amount of parameter space. What could be happening is that the process is based on an maximum-likelihood estimation of a parameter that requires convergence, you could have accidentally (e.g. From time to time, we make mistakes in programming or testing a new R script or function, only to find that R “freezes” and appears to be stuck, or working but giving the impression that it will take an eternity to complete the computation. ![]()
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