estimate.signal.proportion: Estimates signal proportions

In zjdaye/AFNC: Adaptive False Negative Control (AFNC)

Description

estimate.signal.proportion estimates the signal proportion π using a modified estimator based on Meinshausen and Rice (2006). The estimator does not depend on statistical normality assumptions and can adapt to π small. (See Jeng et al. (2016) for details.)

Usage

estimate.signal.proportion(p.value, cd, c0 = NULL, is.sorted = FALSE)

Arguments

p.value	d-vector of p-values
cd	c_d for global Type I error control
c0	maximum number of the most significant p-values considered. If NULL, c0 will be set to \min( \max(5000,0.1*d), d ), such that at least 10% of the p-values are considered.
is.sorted	if p.value is already sorted. If TRUE, p.value will not be re-sorted.

Value

signal.proportion	The estimated signal proportion \hat{π}.
number.signals	The estimated number of signals \hat{s} = \hat{π} * d.

References

Jeng, X.J., Daye, Z.J., Lu, W., and Tzeng, J.Y. (2016) Rare Variants Association Analysis in Large-Scale Sequencing Studies at the Single Locus Level.

Meinshausen M. and Rice, J. (2006) Estimating the proportion of false null hypotheses among a large number of independent tested hypotheses. Ann. Statist., 34:373-393.

Examples

# Load "AFNC" library and example data.
library("AFNC")
data(example_data)

# Estimate signal proportions
set.seed(1)
cd = estimate.cd(d=length(p.value), M=10000, alpha=0.05) # Estimate cd
obj = estimate.signal.proportion(p.value, cd=cd)
signal.proportion = obj$signal.proportion  # Estimated signal proportion
number.signals = obj$number.signals  # Estimated number of signals

zjdaye/AFNC documentation built on May 4, 2019, 11:23 p.m.