simulation.fixed: Simulation function - fixed censoring

Description Usage Arguments Value Author(s) See Also Examples

Description

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

Usage

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

Arguments

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 Cs. This vector must be the same length as Cs.

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)

Value

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 (pava.cs if alpha = beta = 1, pava.cs.mc otherwise)

group.result

Result from hybrid EM-PAV algorithm, see function hybrid.em.pav for details

Author(s)

Lucia Petito

See Also

hybrid.em.pav, pava.cs.mc, gen.data.fixed

Examples

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, 2019, 7:51 a.m.