MLE.perept: MLE.perept

In alex-kalinka-cruk/perept: Performance estimation using repeated tests.

Description

Maximum Likelihood Estimation (MLE) of the performance of a test when there are repeated tests performed on the same subjects.

Usage

MLE.perept(pos_counts, delta_pos = NULL, maxiter = 1e+05, tol = 1e-06)

Arguments

pos_counts	A data frame of positive counts across a set of repeated tests with ‘n' columns. The first column is expected to contain subject IDs, columns 2 to 'n-1' should contain positive counts across subjects for the 'n-2' tests, and the 'n'’th column should contain the total positive count across tests for each subject (the row sums).
delta_pos	A vector of real numbers (in the range [0,1]) giving initial estimates of the probability of each subject being positive - must be the same length as 'n_pos'. If 'NULL', then 'n_pos'/'N' is used. Defaults to 'NULL'.
maxiter	A positive integer specifying the maximum allowable number of iterations without convergence occurring. Defaults to 1e5.
tol	A real number specifying the maximum ODL difference between successive steps of the EM algorithm below which convergence occurs. Defaults to 1e-6.

Value

An object of class 'MLE.perept', which is a list with the following elements:

References

Jakobsdottir and Weeks (2007). Estimating prevalence, false-positive rate, and false-negative rate with use of repeated testing when true responses are unknown. Am J Hum Genet 81:1111-1113.

alex-kalinka-cruk/perept documentation built on Jan. 11, 2020, 3:46 p.m.