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With the latest developement of missing value 'mi' terms, which now can handle measurement error and missingess at the same time, measurement error 'me' terms are now merely a special case of 'mi' terms. Further, 'me' implicitely contribute the meaurement model without this being appropriately accounted for in the modeling syntax or post-processing. Currently, me terms have some advantages for the post-prcoessing with new data, but these advanatages are only caused by the implementation and do not refer to any actual statistical cause.
Once the post-processing of 'mi' has been improved, which basically means allowing for sequential evaluation of predictions of multiple responses, 'me' can be deprecated and eventually removed.
The text was updated successfully, but these errors were encountered:
mi terms are now very close to fully support everything that is offered by me terms in addition to all the other flexibility and consistency that comes with mi. I have decided to soft deprecate me terms, but I don't think it would make sense to actually deprecate it (with the thread to remove it eventually) but rather keep it around for the time being.
With the latest developement of missing value 'mi' terms, which now can handle measurement error and missingess at the same time, measurement error 'me' terms are now merely a special case of 'mi' terms. Further, 'me' implicitely contribute the meaurement model without this being appropriately accounted for in the modeling syntax or post-processing. Currently,
me
terms have some advantages for the post-prcoessing with new data, but these advanatages are only caused by the implementation and do not refer to any actual statistical cause.Once the post-processing of 'mi' has been improved, which basically means allowing for sequential evaluation of predictions of multiple responses, 'me' can be deprecated and eventually removed.
The text was updated successfully, but these errors were encountered: