pava.cs: Variations on PAVA

In lpetito/groupedCS: Simulations to supplement the paper "Misclassified Grouped Current Status Data"

Description

These functions are the variations on the pool-adjacent-violators algorithm needed to compute the NPMLE for current status data (pava.cs) and misclassified current status data (pava.cs.mc).

Usage

pava.cs(Cs, initial)
pava.cs(Cs, initial, alpha, beta)

Arguments

Cs	Observation times for each individual
initial	The test result for each individual (true if alpha and beta are 1, misclassified otherwise)
alpha	Sensitivity. Default is 1.
beta	Specificity. Default is 1.

Value

The output of the PAV (or adjusted PAV) algorithm. This is just a list of the predicted cumulative probabilities at each value of C.

Author(s)

Lucia Petito

Examples

data <- gen.data.weibull.unif(100, 2, 4, 25, 25, 0.9, 0.9)
with(data, pava.cs(data$Cs, data$delta.ind))
with(data, pava.cs.mc(data$Cs, data$y.ind, alpha=0.9, beta=0.9))

lpetito/groupedCS documentation built on May 21, 2019, 7:51 a.m.