Description Usage Arguments Value Author(s) See Also Examples

View source: R/grouped_csdata_weibull.R

This function does one replication of the simulation for the supplemental materials section of the paper from data generated with fixed censoring times and a user-specified true event probability. It returns a description of the misclassification of both the individual and group tests, the results from the appropriate PAVA, the results from the hybrid EM-PAV algorithm for grouped tests, and the number of iterations the hybrid EM-PAV algorithm takes to converge

1 | ```
simulation.fixed(n, k, Cs, true.F, alpha, beta, t)
``` |

`n` |
number of individuals |

`k` |
grouping size |

`Cs` |
a vector of the observed censoring times |

`true.F` |
a vector of event probabilities at each one of the |

`alpha` |
Sensitivity: probability of a positive test results given that the individual is truly diseased (or that the group contains at least one person who is truly diseased). Default is 1 - no misclassification |

`beta` |
Specificity: probability of a negative test results given that the individual is truly not diseased (or that the group contains noone who is truly diseased). Default is 1 - no misclassification |

`t` |
threshold for convergence (default is 0.01) |

The same list as returned by `simulation.random`

`desc.ind` |
Table with description of the misclassification of the individual tests |

`desc.group` |
Table with description of the misclassification of the group tests |

`num.it` |
Number of iterations for the hybrid EM-PAV algorithm to converge |

`ind.result` |
Result from appropriate PAV algorithm ( |

`group.result` |
Result from hybrid EM-PAV algorithm, see function |

Lucia Petito

`hybrid.em.pav`

, `pava.cs.mc`

, `gen.data.fixed`

1 | ```
simulation(100, 2, 1:10, seq(0.05, 0.5, 0.05), 0.95, 0.95, 0.01)
``` |

lpetito/groupedCS documentation built on May 21, 2017, 2:42 p.m.

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