spatialRoc: Sensitivity and specificity

Description Usage Arguments Details Value Author(s) See Also

View source: R/spatialRoc.R

Description

Calculate ROC curves using model fits to simulated spatial data

Usage

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spatialRoc(fit, rr = c(1, 1.2, 1.5, 2), truth, border=NULL, 
	random = FALSE, prob = NULL, spec = seq(0,1,by=0.01))

Arguments

fit

A fitted model from the lgcp function

rr

Vector of relative risks exceedance probabilities will be calculated for. Values are on the natural scale, with spatialRoc taking logs when appropriate.

truth

True value of the spatial surface, or result from simLgcp function. Assumed to be on the log scale if random=TRUE and on the natural scale otherwise.

border

optional, SpatialPolygonsDataFrame specifying region that calculations will be restricted to.

random

compute ROC's for relative intensity (FALSE) or random effect (TRUE)

prob

Vector of exceedance probabilities

spec

Vector of specificities for the resulting ROC's to be computed for.

Details

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.

Value

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.

Author(s)

Patrick Brown

See Also

lgcp, simLgcp, excProb


geostatsp documentation built on July 14, 2018, 9:01 a.m.