binGroup (version 2.2-1)

Array.Measures: Operating characteristics for array testing without master pooling

Description

Calculate the expected number of tests and accuracy measures for each individual using array testing without master pooling

Usage

Array.Measures(p, se, sp)

Arguments

p

matrix of probabilities corresponding to each individual's risk of disease.

se

the sensitivity of the diagnostic test.

sp

the specificity of the diagnostic test.

Value

A list containing:

T

the expected number of tests for the array.

PSe

a matrix containing each individual's pooling sensitivity, corresponding to the input matrix of individual probabilities.

PSp

a matrix containing each individual's pooling specificity, corresponding to the input matrix of individual probabilities.

PPV

a matrix containing each individual's pooling positive predictive value, corresponding to the input matrix of individual probabilities.

NPV

a matrix containing each individual's pooling negative predictive value, corresponding to the input matrix of individual probabilities.

Details

This function calculates the operating characteristics for array testing without master pooling. Operating characteristics calculated are expected number of tests, pooling sensitivity, pooling specificity, pooling positive predictive value, and pooling negative predictive value for each individual.

References

McMahan2012bbinGroup

See Also

MasterPool.Array.Measures for calculating operating characteristics under non-informative array testing with master pooling, hierarchical.desc2 for three-stage hierarchical and non-informative two-stage hierarchical testing, and inf.dorf.measures for informative two-stage hierarchical testing. See p.vec.func for generating a vector of individual risk probabilities for informative group testing and Informative.array.prob for arranging individual risk probabilities in a matrix for informative array testing.

http://chrisbilder.com/grouptesting

Other Operating characteristic functions: MasterPool.Array.Measures, hierarchical.desc2, inf.dorf.measures

Examples

# NOT RUN {
# Calculate the operating characteristics for 
#   non-informative array testing without master
#   pooling, with a 5x5 array and an overall disease 
#   risk of p = 0.02.
# 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.
p.mat <- matrix(data=0.02, ncol=5, nrow=5)
Array.Measures(p=p.mat, se=0.95, sp=0.95)

# Calculate the operating characteristics for 
#   informative array testing without master
#   pooling, with a 3x3 array and an overall disease
#   risk of p = 0.03 and alpha = 2.
# 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(8791)
p.vec <- p.vec.func(p=0.03, alpha=2, grp.sz=9)
p.mat <- Informative.array.prob(prob.vec=p.vec, nr=3, 
nc=3, method="gd")
Array.Measures(p=p.mat, se=0.99, sp=0.99)
# }