spatstat.core (version 2.3-1)

roc: Receiver Operating Characteristic

Description

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

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 slrm
roc(X, …)

Arguments

X

Point pattern (object of class "ppp" or "lpp") or fitted point process model (object of class "ppm", "kppm", "slrm" 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.

Value

Function value table (object of class "fv") which can be plotted to show the ROC curve.

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).

(For spatial logistic regression models (class "slrm") replace “intensity” by “probability of presence” in the text above.)

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

auc

Examples

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