combined.fdr: Calculating combined false positive rate based on Monte Carlo...
In xiashen/triangulation: Triangulation of test results from multiple methods

Description Usage Arguments Value Note Author(s) References See Also Examples

View source: R/combined.fdr.R

The function returns the estimated false discovery rate for each category of testing results, given the operating characteristics of different testing methods.

1	combined.fdr(sens, specs, prev, size = 1e+06)

`sens`	A vector of K sensitivity values for K different testing methods.
`specs`	A vector of K specificity values for K different testing methods, with the order of the methods same as in `sens`.
`prev`	Prevalence of true positives.
`size`	A large integer for Monte Carlo sampling. Default: 1 million.

FDR estimates for each category of testing results combination.

The function is based on Monte Carlo sampling estimation. Make sure a sufficiently large size is given.

Zhijian Yang, Xia Shen

Yang Z, Xu W, Zhai R, Li T, Ning Z, Pawitan Y, Shen X (2020). Triangulation of analysis strategies links complex traits to specific tissues and cell types. Submitted.

triangulate

## Not run: 

combined.fdr(c(.5,.6,.7), c(.5,.4,.3), .5)
#        000        001        010        011        100        101        110        111
# 0.50135036 0.50012141 0.49931784 0.49982606 0.49945863 0.49953203 0.50014472 0.50040443

## End(Not run)