How predicted patch probability moves as one predictor is swept across its range, with every other predictor held at the values it actually takes. This is a partial dependence curve: for each value on the sweep, the predictor is set to that value for every station, the model is asked for a probability, and those are averaged.
Arguments
- fitted
a fitted
workflows::workflow()- model_data
the data it was fitted on
- predictors
predictor column names
- grid_size
how many points to sweep each predictor across
- max_rows
stations to average over; the full set when smaller
Details
Importance says a predictor matters; this says what it does — whether probability rises, falls, peaks in the middle, or bends somewhere particular. That is most of what a habitat model is fitted to find out.
Computed by prediction rather than read out of the fitted object, so it means
the same thing for all four model types and the curves can be laid against
each other. A GAM's own smooths are the exact version of this for a GAM alone
— gam_smooth_terms() reports those — but they are on the log-odds scale and
cannot be compared with a forest.
What it hides
Averaging over the other predictors is what makes one curve per predictor possible, and it is also the limitation: an effect that reverses depending on another predictor averages to something flatter than either case, and the plot cannot show that it did. Two correlated predictors are also swept into combinations the ocean never produces.
References
Friedman JH (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics 29(5), 1189-1232. doi:10.1214/aos/1013203451 — where partial dependence comes from, section 8.2