auc.ppm {spatstat.model} | R Documentation |
Area Under ROC Curve
Description
Compute the AUC (area under the Receiver Operating Characteristic curve) for a fitted point process model.
Usage
## S3 method for class 'ppm'
auc(X, ..., subset=NULL)
## S3 method for class 'kppm'
auc(X, ..., subset=NULL)
## S3 method for class 'slrm'
auc(X, ..., subset=NULL)
Arguments
X |
Fitted point process model
(object of class |
... |
Arguments passed to |
subset |
Optional. A spatial window (object of class |
Details
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.)
The algorithm also calculates the theoretically expected AUC value for this model, as described in Baddeley et al (2025).
Value
Numeric vector of length 2 giving the AUC value and the theoretically expected AUC value for this model.
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..
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.
See Also
Examples
fit <- ppm(swedishpines ~ x+y)
auc(fit)
auc(swedishpines, "x")