Dear R-users,
I've spent most of the day reading R documentation at length but
couldn't
find something perhaps obvious.
I have a dataset made of 3 morphometric variables for a series of watershed
[log(slope); log(drainage_area); distance_to_outlet]
My aim is to predict the value of log(slope) for pairs of [drainage_area;
distance_to_outlet] (sounds like a plain linear model fitting, right,
nothing too fancy there). In the literature, the standard procedure is to
reduce the number of observations by computing the mean value of slope
insides bins of log(drainage_area) and distance_to_outlet respectively.
Usually, we start with dispersed point clouds and try desperately to reduce
the scatter, hence this binning and averaging procedure.
How would you go about this?
That is:
1. how does one create bins in two (or n?) dimensions?
2. how does one how does one compute the mean(or median) value of log(slope)
inside each bin?
Any clue is welcome
Thomas
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