Computes the Receiver Operating Characteristic curve for a point pattern or a fitted point process model on a linear network.
## S3 method for class 'lpp' roc(X, covariate, ..., high = TRUE) ## S3 method for class 'lppm' roc(X, ...)
Point pattern on a network (object of class
Spatial covariate. Either a
Arguments passed to
Logical value indicating whether the threshold operation should favour high or low values of the covariate.
computes the Receiver Operating
The area under the ROC is computed by
roc is generic,
with methods for
"ppm" described in the help file
This help file describes the methods for classes
For a point pattern
X and a covariate
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
which can be plotted to show the ROC curve.
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(spiders, "x")) fit <- lppm(spiders ~ x) plot(roc(fit))
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