summary.opChar: Summary method for operating characteristics results

Description

Produce a summary list for objects of class "opChar" returned by operatingCharacteristics1 (opChar1) or operatingCharacteristics2 (opChar2).

Usage

# S3 method for opChar
summary(object, ...)

Arguments

object

an object of class "opChar", providing the calculated operating characteristics for a group testing algorithm.

...

currently not used.

Value

summary.opChar returns an object of class "summary.opChar", a list containing:

Algorithm

character string specifying the name of the group testing algorithm.

Configuration

matrix detailing the configuration from object. For hierarchical testing, this includes the group sizes for each stage of testing. For array testing, this includes the array dimension (row/column size) and the array size (the total number of individuals in the array).

Tests

matrix detailing the expected number of tests and expected number of tests per individual from object

Accuracy

a list containing:

Individual: matrix detailing the accuracy measures for each individual from object (for objects returned by opChar1).
Disease 1 Individual: matrix detailing the accuracy measures pertaining to disease 1 for each individual from object (for objects returned by opChar2).
Disease 2 Individual: matrix detailing the accuracy measures pertaining to disease 2 for each individual from object (for objects returned by opChar2).
Overall: matrix detailing the overall accuracy measures for the algorithm from object.

Details

This function produces a summary list for objects of class "opChar" returned by operatingCharacteristics1 (opChar1) or operatingCharacteristics2 (opChar2). It formats the testing configuration, expected number of tests, expected number of tests per individual, and accuracy measures.

The Configuration component of the result gives the testing configuration, which may include the group sizes for each stage of a hierarchical testing algorithm or the row/column size and array size for an array testing algorithm. The Tests component of the result gives the expected number of tests and the expected number of tests per individual for the algorithm.

The Accuracy component gives the individual accuracy measures for each individual in object and the overall accuracy measures for the algorithm. Accuracy measures included are the pooling sensitivity, pooling specificity, pooling positive predictive value, and pooling negative predictive value. The overall accuracy measures displayed are weighted averages of the corresponding individual accuracy measures for all individuals in the algorithm. Expressions for these averages are provided in the Supplementary Material for Hitt et al. (2019). For more information, see the Details' section for the operatingCharacteristics1 (opChar1) or operatingCharacteristics2 (opChar2) function.

Examples

Run this code

# NOT RUN {
# Calculate the operating characteristics for 
#   non-informative four-stage hierarchical testing.
config.mat <- matrix(data = c(rep(1, 24), rep(1, 16), rep(2, 8), 
                            rep(1, 8), rep(2, 8), rep(3, 4), 
                            rep(4, 2), rep(5, 2), 1:24), 
                     nrow = 4, ncol = 24, byrow = TRUE)
calc1 <- opChar1(algorithm = "D4", p = 0.01, Se = 0.99, Sp = 0.99, 
                 hier.config = config.mat, a = c(1, 9, 17, 21, 23))
summary(calc1)

# Calculate the operating characteristics for 
#   informative array testing without master pooling.
calc2 <- opChar1(algorithm = "IA2", p = 0.025, alpha = 0.5, 
                 Se = 0.95, Sp = 0.99, rowcol.sz = 12)
summary(calc2)

# Calculate the operating characteristics for 
#   informative two-stage hierarchical testing.
config.mat <- matrix(data = c(rep(1, 5), rep(2, 4), 1, 1:10), 
                     nrow = 2, ncol = 10, byrow = TRUE)
Se <- matrix(data = c(rep(0.95, 2), rep(0.99, 2)), 
             nrow = 2, ncol = 2, byrow = FALSE)
Sp <- matrix(data = c(rep(0.96, 2), rep(0.98, 2)), 
             nrow = 2, ncol = 2, byrow = FALSE)
calc3 <- opChar2(algorithm = "ID2", alpha = c(18.25, 0.75, 0.75, 0.25), 
                 Se = Se, Sp = Sp, hier.config = config.mat)
summary(calc3)

# Calculate the operating characteristics for 
#   non-informative array testing with master pooling.
calc4 <- opChar2(algorithm = "A2M", p.vec = c(0.92, 0.05, 0.02, 0.01), 
                 Se = rep(0.95, 2), Sp = rep(0.99, 2), rowcol.sz = 8)
summary(calc4)
# }