Compute the AUC (area under the Receiver Operating Characteristic curve) for a fitted point process model.
# S3 method for ppm
auc(X, ...)# S3 method for kppm
auc(X, ...)
# S3 method for slrm
auc(X, ...)
Numeric.
For auc.ppm, auc.kppm and auc.lppm, the result is a
numeric vector of length 2 giving the AUC value
and the theoretically expected AUC value for this model.
Point pattern (object of class "ppp" or "lpp")
or fitted point process model (object of class "ppm",
"kppm", "slrm" or "lppm").
Arguments passed to as.mask controlling the
pixel resolution for calculations.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner rolfturner@posteo.net and Ege Rubak rubak@math.aau.dk.
This command computes the AUC, the area under the Receiver Operating
Characteristic curve. The ROC itself is computed by roc.
For a fitted point process model X,
the AUC measures the ability of the
fitted model intensity to separate the spatial domain
into areas of high and low density of points.
Suppose \(\lambda(u)\) is the intensity function of the model.
The AUC is the probability that
\(\lambda(x_i) > \lambda(U)\).
That is, AUC is the probability that a randomly-selected data point
has higher predicted intensity than does a randomly-selected spatial
location.
The AUC is not a measure of the goodness-of-fit of the model
(Lobo et al, 2007).
(For spatial logistic regression models (class "slrm")
replace “intensity” by “probability of presence”
in the text above.)
Lobo, J.M., Jimenez-Valverde, A. and Real, R. (2007) AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography 17(2) 145--151.
Nam, B.-H. and D'Agostino, R. (2002) Discrimination index, the area under the ROC curve. Pages 267--279 in Huber-Carol, C., Balakrishnan, N., Nikulin, M.S. and Mesbah, M., Goodness-of-fit tests and model validity, Birkhauser, Basel.
roc
fit <- ppm(swedishpines ~ x+y)
auc(fit)
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