plot.OTC {binGroup2} | R Documentation |

Produce a plot for objects of class `"OTC"`
returned by `OTC1`

or `OTC2`

.

## S3 method for class 'OTC' plot(x, ...)

`x` |
an object of class |

`...` |
currently not used. |

This function produces a plot for objects of class `"OTC"`
returned by `OTC1`

or `OTC2`

. It plots the expected
number of tests per individual for each similar testing configuration
in the object.

In addition to the OTC, the `OTC1`

and `OTC2`

functions provide operating characteristics for other configurations
corresponding to each initial group size provided by the user. For
algorithms where there is only one configuration for each initial group size
(non-informative two-stage hierarchical and all array testing algorithms),
results for each initial group size are plotted. For algorithms where there
is more than one possible configuration for each initial group size
(informative two-stage hierarchical and all three-stage hierarchical
algorithms), the results corresponding to the best configuration for each
initial group size are plotted.

If a single value is provided for the `group.sz` argument in the
`OTC1`

or `OTC2`

functions, no plot will be
produced.

The plot is produced using the `ggplot2`

package. Customization
features from `ggplot2`

are available once the package is loaded.
Examples are shown in the 'Examples' section.

A plot of the expected number of tests per individual for similar configurations provided in the object.

Brianna D. Hitt

`OTC1`

and `OTC2`

for creating an object of class
`"OTC"`.

# Estimated running time for all examples was calculated # using a computer with 16 GB of RAM and one core of # an Intel i7-6500U processor. Please take this into # account when interpreting the run time given. # Find the optimal testing configuration for # non-informative two-stage hierarchical testing. res1 <- OTC1(algorithm = "D2", p = 0.01, Se = 0.99, Sp = 0.99, group.sz = 3:100, obj.fn = c("ET", "MAR", "GR1"), weights = matrix(data = c(1, 1), nrow = 1, ncol = 2)) plot(res1) # Customize the plot using the ggplot2 package. library(ggplot2) plot(res1) + ylim(0,1) + ggtitle("Similar configurations for Dorfman testing") + theme(plot.title = element_text(hjust = 0.5)) # Find the optimal testing configuration for # informative three-stage hierarchical testing res2 <- OTC1(algorithm = "ID3", p = 0.025, Se = c(0.95, 0.95, 0.99), Sp = c(0.96, 0.96, 0.98), group.sz = 3:15, obj.fn = "ET", alpha = 2) plot(res2) # Find the optimal testing configuration for # informative array testing without master pooling. # This example takes approximately 30 seconds to run. res3 <- OTC1(algorithm = "IA2", p = 0.05, alpha = 2, Se = 0.90, Sp = 0.90, group.sz = 3:20, obj.fn = "ET") plot(res3) # Find the optimal testing configuration for # informative two-stage hierarchical testing. # This example takes approximately 30 seconds to run. 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) res4 <- OTC2(algorithm = "ID2", alpha = c(18.25, 0.75, 0.75, 0.25), Se = Se, Sp = Sp, group.sz = 12:20) plot(res4) # Find the optimal testing configuration for # non-informative three-stage hierarchical testing. # This example takes approximately 1 minute to run. Se <- matrix(data = c(rep(0.95, 6)), nrow = 2, ncol = 3) Sp <- matrix(data = c(rep(0.99, 6)), nrow = 2, ncol = 3) res5 <- OTC2(algorithm = "D3", p.vec = c(0.95, 0.0275, 0.0175, 0.005), Se = Se, Sp = Sp, group.sz = 5:20) plot(res5) # Find the optimal testing configuration for # non-informative array testing with master pooling. # This example takes approximately 10 seconds to run. res6 <- OTC2(algorithm = "A2M", p.vec = c(0.90, 0.04, 0.04, 0.02), Se = rep(0.99, 2), Sp = rep(0.99, 2), group.sz = 3:20) plot(res6)

[Package *binGroup2* version 1.1.0 Index]