calculateThreshold: Calculate PPA significance threshold leading to a desired...

Description Usage Arguments Value References Examples

View source: R/calculateThreshold.R

Description

In the context of multiple testing and discoveries, a popular approach is to use a common threshold leading to a desired false discovery rate (FDR). In the Bayesian paradigm, derivation of the PPA threshold is trivial and can be calculated using a direct posterior probability calculation as described in Newton et al. (2004).

Usage

1
calculateThreshold(prob, threshold)

Arguments

prob

matrix or data frame that contains Posterior Probability of Association (output of eqtlMcmc function).

threshold

The desired false discovery rate.

Value

cutoff

The significance threshold value

References

Newton, MA., Noueiry, A., Sarkar, D. and Ahlquist, P. (2004): "Detecting differential gene expression with a semiparametric hierarchical mixture method."Biometrics, 5(2), 155-176

Examples

1
2
data(PPA.liver)
cutoff.liver <- calculateThreshold(PPA.liver, 0.2)

iBMQ documentation built on Nov. 8, 2020, 11:04 p.m.