accuracy.dorf: Accuracy measures for informative Dorfman testing

In binGroup: Evaluation and Experimental Design for Binomial Group Testing

Description

Calculate the accuracy measures for each individual in a pool used with informative Dorfman testing.

Usage

accuracy.dorf(p, se, sp)

Arguments

p	a vector of each individual's probability of infection.
se	the sensitivity of the diagnostic test.
sp	the specificity of the diagnostic test.

Details

This function calculates the pooling sensitivity, pooling specificity, pooling positive predictive value, and pooling negative predictive value for each individual belonging to a pool of size greater than or equal to one used with informative Dorfman testing. Calculations of these measures are done using the equations presented in McMahan et al. (2012).

Value

a list containing:

PSe	a vector containing each individual's pooling sensitivity.
PSp	a vector containing each individual's pooling specificity.
PPV	a vector containing each individual's pooling positive predictive value.
NPV	a vector containing each individual's pooling negative predictive value.

Author(s)

This function was originally written by Christopher S. McMahan for McMahan et al. (2012). The function was obtained from http://chrisbilder.com/grouptesting.

References

\insertRef

McMahan2012abinGroup

See Also

http://chrisbilder.com/grouptesting

Other Informative Dorfman functions: characteristics.pool, inf.dorf.measures, opt.info.dorf, opt.pool.size, pool.specific.dorf, thresh.val.dorf

Examples

# This example takes less than 1 second to run.
# Estimated running time was calculated using a 
#   computer with 16 GB of RAM and one core of an 
#   Intel i7-6500U processor.
set.seed(8135)
p.vec <- p.vec.func(p=0.02, alpha=1, grp.sz=10)
accuracy.dorf(p=p.vec[1:3], se=0.90, sp=0.90)

Example output

$PSe
[1] 0.81 0.81 0.81

$PSp
[1] 0.9891125 0.9892919 0.9894942

$PPV
[1] 0.1318176 0.2459963 0.3464342

$NPV
[1] 0.9996081 0.9991723 0.9986816

binGroup documentation built on May 2, 2019, 8:57 a.m.