I am trying to create a conversion factor for zooplankton catch to convert between an accurate (hereafter referred to as actual abundance) and inaccurate (hereafter referred to as estimated abundance) sampling method. We know the estimated abundance is influenced by a number of covariates that partially determine the accuracy of this method. Ultimately, we want to produce a model that we can use to predict the actual abundance given the estimated abundance and the covariates.
However, the issue is that the thing we want to predict (actual abundance) should logically be included as the predictor in a model modeling the response variable: estimated abundance. This is because the covariates influence estimated abundance, but should have no effect on the actual abundance, and the covariates likely interact with the actual abundance to determine the estimated abundance number.
My model is:
estimated abundance ~ actual abundance * covariates
estimated abundance is zero-inflated so I am using a hurdle_lognormal distribution.
Is there any way to use brms (or any other stan-related R package) to use this fitted model to predict actual abundance from a new dataset of my estimated abundances and covariates?
- Operating System: Win 10
- brms Version: 2.9.0