simulation.random: Simulation function - random censoring

Description Usage Arguments Value Author(s) See Also Examples

View source: R/grouped_csdata_weibull.R

Description

This function does one replication of the simulation for the paper from data generated with Weibull event times and Uniform censoring. 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(n, k, shape, scale, quantile, x, alpha=1, beta=1, t=0.01)

Arguments

n

number of individuals

k

grouping size

shape

shape for the Weibull distribution

scale

scale for the Weibull distribution (defaults to 1)

quantile

The maximum probability of the event in the population (default is 0.99)

x

The maximum value for censoring. This should correspond with the quantile argument.

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

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.weibull.unif

Examples

1
simulation(100, 2, 4, 25, 0.25, 14, 0.95, 0.95, 0.01)

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