Hello ecolog, I am working on a power analysis simulation for long-term forest monitoring data, with the goal of documenting our power to detect trends over time. The simulation is based on a repeated measures hierarchical model, where future data is simulated based on the initial data set and a bootstrap of pilot data differences between observation periods, multiplied by a range of effect sizes (50% decline to 50% increase).
My question is about the appropriate transformation to use for percent cover data in this simulation. I dont want to use raw percentages because the simulation will easily result in proportions less than zero or greater than one. Similarly, a log transform can easily result in back-transformed proportions greater than one. Most other transforms Ive looked at would not prevent back-transformed data from exceeding one or the other boundaries. The exception is the logistic transform, which would indeed force all simulated data to be between zero and one when back-transformed. However, the logistic transform gives values of negative infinity for a percent cover of zero, and positive infinity for a percent cover of one. I was thinking that adding a tiny number to zeros and subtracting a tiny number from ones (e.g., 0.00001) would solve the problem (roughly equivalent to a log of x+1 transform), but I have been unable to find reference to anyone using this approach for percent cover data. Does anyone have any thoughts about the validity of my proposed approach or of another approach that would help solve my problem? Thanks! Brian Mitchell NPS Northeast Temperate Network Program Manager Adjunct Assistant Professor, University of Vermont [email protected]
