roc(X, ...)## S3 method for class 'ppp':
roc(X, covariate, \dots, high = TRUE)
## S3 method for class 'ppm':
roc(X, \dots)
"ppp")
or fitted point process model (object of class "ppm").function(x,y),
a pixel image (object of class "im"), or
one of the strings "x" or "y" indicating the
Cartesian coordinates.as.mask controlling the
pixel resolution for calculations."fv")
which can be plotted to show the ROC curve.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).
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,
aucplot(roc(swedishpines, "x"))
fit <- ppm(swedishpines ~ x+y)
plot(roc(fit))Run the code above in your browser using DataLab