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

In ROC: utilities for ROC, with microarray focus

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",
       quiet=TRUE)

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
quiet	defaults to TRUE, suppressing message about discovery of NA in cutpts

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).

Value

an object of S4 class rocc

Author(s)

Vince Carey ([e-mail])

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)
  

Example output

NA in cutpts forces recomputation using smallest gap
NA in cutpts forces recomputation using smallest gap
NA in cutpts forces recomputation using smallest gap
NA in cutpts forces recomputation using smallest gap

ROC documentation built on Nov. 8, 2020, 5:23 p.m.