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I am using a random forest regression to model a count of species from a number of different survey areas. Each survey area has a different size.

My question is how to model the response variable, to account for these scaling effects? Should I scale the Count prior to fitting the model?

Count <- Count / Survey_area_km2
model < fit(Count ~ pred1 + pred2 + ...)

And then when predicting over a set of survey areas, adjust the response variable prediction by the size of the survey areas I am trying to predict. This will give a count that is in proportion to each survey area?

Count_predictions <- predict(model, prediction_areas)
Rescaled_Counts <- Count_predictions * prediction_areas
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    $\begingroup$ The usual model in your situation is a Poisson rate regression (search this site!), that is, log link function and log area as offset. See also stats.stackexchange.com/questions/50786/… $\endgroup$
    – kjetil b halvorsen ♦
    Jul 18, 2020 at 18:24
  • $\begingroup$ ... and this stored google search $\endgroup$
    – kjetil b halvorsen ♦
    Jul 18, 2020 at 18:25
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    $\begingroup$ Thanks. This is helpful. I hadn't realised there was a function gbm.fit that allows specification for an offset - it doesn't seem feasible if just using gbm. So when I use this the model fit is calculated on the log link scale? Therefore I should define it as model <- gbm.fit(x=predictors, y=response, offset=log(Area)) ? $\endgroup$
    – Anthony W
    Jul 19, 2020 at 10:50
  • $\begingroup$ You can find more information here. $\endgroup$
    – kjetil b halvorsen ♦
    Jul 19, 2020 at 16:54

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