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Part of My Ph.D. Dissertation at University of South Carolina

Introduction

Medical researchers are often interested in modeling the disease infection status of individuals to identify important risk factors and to estimate subject-specific risk probabilities. In many cases, pooling specimens (e.g., blood, urine, swabs, etc.) through group testing offers a novel approach to significantly reduce the number of tests, the time expended, and the overall costs. This has led to the adoption of group testing in a number of infectious disease applications

Libraries

require(MASS)
require(Matrix)
require(splines2)
require(lsei)
require(crayon)

Usage

.
+-- R/
+-- output/
|   +-- application/
|       +-- figures/
|       +-- output_(seednumber).md
|       +-- output_(seednumber).csv
|   +-- simulation/
|       +-- figures/
|       +-- output_(seednumber).md
|       +-- output_(seednumber).csv
+-- run.r
+-- README.md

Open run.r in R or RStudio and it will generate outputs.

Example

An illustrative example is provided. With the default simulation setting and seed number set.seed(1452), we could obtain results in the following table. Its convergence is much faster classical EM algorithm and will converge to global minimal (cost) more precisely.

-----------------------------------------------------------
                          Setting
-----------------------------------------------------------
N                 : 5000
ord               : 5
niknots           : 10
Se                : 0.95 0.95
Sp                : 0.95 0.95
true beta         : 2.0000 -1.0000 -3.0000 4.0000 0.0000
true delta        : 0.3000
-----------------------------------------------------------
                 Accelerated EM Algorithm

Conclusions

To be continue.