Hi list!


I have a set of points representing buildings with two covariates: the first
one being distance to the coast, the second distance to the roads.

2 ks tests, one for each covariate, shows that points are not randomly
organized according to these covariates

But I'd like to know which covariate contributes the most to "explaining" my
points localization..

I thought of using a fitted model then comparing coefficients, something
like
fit <- ppm(buildings, ~dcoast + droad, covariates = list(dcoast = dcoast,
droad = droad))
If coeff. for dcoast is bigger than the one, then the buildings localization
would be more sensitive to the distance to the coast?


Does it seem convenient to you? Any idea?

Mathieu

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