addROC {spatstat.model} | R Documentation |
ROC Curves for Single Term Additions to a Model
Description
Given a fitted point process model, consider adding new explanatory variables, and compute the ROC curve for each new variable.
Usage
addROC(object, scope, high=TRUE, ...)
Arguments
object |
A fitted point process model (object of class |
scope |
A formula or a character vector specifying the variable or variables to be considered for addition, or a fitted point process model containing all of these variables. |
high |
Argument passed to |
... |
Arguments passed to |
Details
This function is like add1
in that it considers each possible term that could be
added to the model object
(or only the terms listed in the scope
argument),
adds each such term to the model, and measures the change in the model.
In this case the change is measured by computing the ROC curve
for the added covariate, using the original model object
as a
baseline.
Either object
or scope
should be a fitted point process
model, and the other argument may be a fitted point process model or a
formula. If object
is a fitted model then scope
may be a
character vector of the names of variables to be added.
Value
A named list containing the ROC curves for each new explanatory variable.
The individual entries belong to class "fv"
,
so they can be plotted.
The list belongs to the class "anylist"
so it can be plotted in its entirety.
Author(s)
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Ege Rubak rubak@math.aau.dk and Suman Rakshit Suman.Rakshit@curtin.edu.au.
References
Baddeley, A., Rubak, E., Rakshit, S. and Nair, G. (2025) ROC curves for spatial point patterns and presence-absence data. doi:10.48550/arXiv.2506.03414.
See Also
Examples
dimyx <- if(interactive()) NULL else 32
fit0 <- ppm(bei ~ 1, data=bei.extra)
z <- addROC(fit0, . ~ grad + elev, dimyx=dimyx)
plot(z)
## how to compute AUC for each curve
sapply(z, auc)