There is an m.scale argument in bma that allows users to record how the data were transformed before going into the model. This argument is then used to determine the back-transformation for the indicator.
In effect, it means that the indicator values are on the odds scale, not the occupancy scale.
This has caused an external user to misinterpret the results: these are fitted values, so it makes sense for them to be on the same scale as the measurements.
Before making any change we should evaluate the implications. Especially, how are the indicators are currently being run? I think they are using log transformed occupancy data, i.e. m.scale="log", such that the resulting index is the geometric mean occupancy.
There is an
m.scaleargument inbmathat allows users to record how the data were transformed before going into the model. This argument is then used to determine the back-transformation for the indicator.Originally, if
m.scale="logit"then the inverse logit transformation was used, such that the resulting index would be bounded between 0 and 1 (as the original data going in). However, we changed this to an exponentiation https://github.com/BiologicalRecordsCentre/BRCindicators/blob/31b7dc69a1fe88719e0c26f4a94a6de3f0dbfcbe/R/bayesian_meta_analysis.R#L304In effect, it means that the indicator values are on the odds scale, not the occupancy scale.
This has caused an external user to misinterpret the results: these are fitted values, so it makes sense for them to be on the same scale as the measurements.
Before making any change we should evaluate the implications. Especially, how are the indicators are currently being run? I think they are using log transformed occupancy data, i.e.
m.scale="log", such that the resulting index is the geometric mean occupancy.