Receiver Operating Characteristic
Computes the Receiver Operating Characteristic curve for a point pattern or a fitted point process model.
roc(X, ...) ## S3 method for class 'ppp' roc(X, covariate, ..., high = TRUE) ## S3 method for class 'ppm' roc(X, ...) ## S3 method for class 'kppm' roc(X, ...)
X |
Point pattern (object of class |
covariate |
Spatial covariate. Either a |
... |
Arguments passed to |
high |
Logical value indicating whether the threshold operation should favour high or low values of the covariate. |
This command computes Receiver Operating
Characteristic curve. The area under the ROC is computed by auc.
For a point pattern X and a covariate Z, the
ROC is a plot showing the ability of the
covariate to separate the spatial domain
into areas of high and low density of points.
For each possible threshold z, the algorithm calculates
the fraction a(z) of area in the study region where the
covariate takes a value greater than z, and the
fraction b(z) of data points for which the covariate value
is greater than z. The ROC is a plot of b(z) against
a(z) for all thresholds z.
For a fitted point process model, the ROC shows the ability of the fitted model intensity to separate the spatial domain into areas of high and low density of points. The ROC is not a diagnostic for the goodness-of-fit of the model (Lobo et al, 2007).
Function value table (object of class "fv")
which can be plotted to show the ROC curve.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.
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.
plot(roc(swedishpines, "x")) fit <- ppm(swedishpines ~ x+y) plot(roc(fit))
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