I have a rather large dataset that takes forever to run the olsrr package's "ols_step_all_possible" function. In fact, it crashes my computer through the process.
I've seen that parallel processing may be a way to rectify this situation using foreach and doparallel packages. Can anyone help with the sample code to do this?
I have a linear regression model, df "sample" here comprised of data from a csv, "learner":
sample = lm(dvar ~ one + two + three + four, data = learner)
to run all possible combinations of this model to find the best model, I've been using this function from olsrr:
final = ols_step_all_possible(sample)
