jmboehm
commented
1 year ago One additional change I considered but did not implement is that the biascorrection tests often need the y and/or mu values, expecting these to be in the augment_df argument. Saving them all the time in order to calculate pseudo-r2 might make it unnecessary to keep those in the augment_df
Thanks. There are two disadvantages that I see: (1) we store much more data in the estimated model (I'd prefer them to be as light as necessary, in contrast to e.g. stargazer etc); (2) we need to specify the type (you coded y and mu as Vector{Float64}, whereas with lower-end GPUs you may prefer Float32s). So I'd suggest that we return those statistics only if the necessary information has been saved in augmentdf.
junder873
codecov[bot]
This adds support for calculating pseudo-r2 values. To do so, functions for loglikelihood and nullloglikelihood are added and the output type needs to store a few extra variables (y, mu and a dof).
One additional change I considered but did not implement is that the biascorrection tests often need the y and/or mu values, expecting these to be in the augment_df argument. Saving them all the time in order to calculate pseudo-r2 might make it unnecessary to keep those in the augment_df