ROC (version 1.48.0)

rocdemo.sca: function to build objects of class 'rocc'

Description

rocdemo.sca -- demonstrate 'rocc' class construction using a scalar marker and simple functional rule

Usage

rocdemo.sca(truth, data, rule=NULL,  cutpts=NA, markerLabel="unnamed marker", caseLabel="unnamed diagnosis")

Arguments

truth

true classification of objects. Must take values 0 or 1.

data

quantitative markers used to classify

rule

rule: a function with arguments (x, thresh) returning 0 or 1. If no rule is provided or the standard rule dxrule.sca is passed, a faster C-based implementation is used to compute sensitivity and specificity.

cutpts

values of thresholds; no NA allowed, or they will be recomputed using smallest gap between data points with distinct values

markerLabel

textual label describing marker

caseLabel

textual label describing classification

Value

an object of S4 class rocc

Details

dxrule.sca is function (x, thresh) ifelse(x > thresh, 1, 0)

The default value of argument cutpts is a point less than min(data), points separating the unique values of data and a point greater than max(data).

References

See Also

AUC

Examples

set.seed(123)
R1 <- rocdemo.sca( rbinom(40,1,.3), rnorm(40), caseLabel="new case", markerLabel="demo Marker" )
plot(R1, line=TRUE, show.thresh=TRUE)

truth <- c(0, 1, 0, 1, 1, 0, 1, 1)
data <- c(2, 3, 4, 4, 5, 6, 7, 8)
R2 <- rocdemo.sca(truth, data, dxrule.sca)
plot(R2, line=TRUE, show.thresh=TRUE)
R3 <- rocdemo.sca(truth, data, function(x, thresh) 1 - dxrule.sca(x, thresh))
if (AUC(R2) + AUC(R3) != 1) stop('Sum of AUCs should be 1.')
#
# more involved
#
set.seed(1234)
x = runif(1000)
w = runif(1000)
z = rbinom(1000, 1, plogis(-2.7+6.2*x + .3*w))
m1 = glm(z~x, fam=binomial)
demorule.glm.clo = function(model) function(w,thresh)
 ifelse(predict(model, newdata=list(x=w), type="response")>thresh, 1, 0)
demorule.glm = demorule.glm.clo(m1)
R4 = rocdemo.sca(z, x, demorule.glm )
plot(R4)