spatialRoc | R Documentation |
Calculate ROC curves using model fits to simulated spatial data
spatialRoc(fit, rr = c(1, 1.2, 1.5, 2), truth, border=NULL,
random = FALSE, prob = NULL, spec = seq(0,1,by=0.01))
fit |
A fitted model from the |
rr |
Vector of relative risks exceedance probabilities will be calculated for. Values
are on the natural scale, with |
truth |
True value of the spatial surface, or result from |
border |
optional, |
random |
compute ROC's for relative intensity ( |
prob |
Vector of exceedance probabilities |
spec |
Vector of specificities for the resulting ROC's to be computed for. |
Fitted models are used to calculate exceedance probabilities, and
a location is judged to be above an rr
threshold if this
exceedance probability is above a specified probability threshold.
Each raster cell of the true surface is categorized as being either true positive, false
positive, true negative, and false negative and sensitivity and specificity computed.
ROC curves are produced by varying the probability threshold.
An array, with dimension 1 being probability threshold, dimension 2
being the relative risk threshold, dimension 3 being sensitivity and specificity.
If fit
is a list of model fits, dimension 4 corresponds to elements of fit
.
Patrick Brown
lgcp
, simLgcp
, excProb
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