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
require(MASS)
require(Matrix)
require(splines2)
require(lsei)
require(crayon)
.
+-- 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.
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.
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Setting
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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
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Accelerated EM Algorithm
To be continue.