# roc

##### Receiver Operating Characteristic

Computes the Receiver Operating Characteristic curve for a point pattern or a fitted point process model.

- Keywords
- spatial

##### Usage

`roc(X, …)`# S3 method for ppp
roc(X, covariate, …, high = TRUE)

# S3 method for ppm
roc(X, …)

# S3 method for kppm
roc(X, …)

# S3 method for lpp
roc(X, covariate, …, high = TRUE)

# S3 method for lppm
roc(X, …)

##### Arguments

- X
Point pattern (object of class

`"ppp"`

or`"lpp"`

) or fitted point process model (object of class`"ppm"`

or`"kppm"`

or`"lppm"`

).- covariate
Spatial covariate. Either a

`function(x,y)`

, a pixel image (object of class`"im"`

), or one of the strings`"x"`

or`"y"`

indicating the Cartesian coordinates.- …
Arguments passed to

`as.mask`

controlling the pixel resolution for calculations.- high
Logical value indicating whether the threshold operation should favour high or low values of the covariate.

##### Details

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).

##### Value

Function value table (object of class `"fv"`

)
which can be plotted to show the ROC curve.

##### References

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

```
# NOT RUN {
plot(roc(swedishpines, "x"))
fit <- ppm(swedishpines ~ x+y)
plot(roc(fit))
# }
```

*Documentation reproduced from package spatstat, version 1.57-1, License: GPL (>= 2)*