FDRestimation (version 1.0.0)

get.pi0: pi0 Estimation

Description

This function estimates the null proportion of data or pi0 value.

Usage

get.pi0(
  pvalues,
  set.pi0 = 1,
  zvalues = "two.sided",
  estim.method = "last.hist",
  threshold = 0.05,
  default.odds = 1,
  hist.breaks = "scott",
  na.rm = TRUE
)

Arguments

pvalues

A numeric vector of raw p-values.

set.pi0

A numeric value to specify a known or assumed pi0 value in the interval [0,1]. Defaults to 1. Which means the assumption is that all inputted raw p-values come from the null distribution.

zvalues

A numeric vector of z-values to be used in pi0 estimation or a string with options "two.sided", "greater" or "less". Defaults to "two.sided".

estim.method

A string used to determine which method is used to estimate the pi0 value. Defaults to "last.hist".

threshold

A numeric value in the interval [0,1] used in a multiple comparison hypothesis tests to determine significance from the null. Defaults to 0.05.

default.odds

A numeric value determining the ratio of pi1/pi0 used in the computation of lower bound FDR. Defaults to 1.

hist.breaks

A numeric or string variable representing how many breaks in the pi0 estimation histogram methods. Defaults to "scott".

na.rm

A Boolean TRUE or FALSE value indicating whether NA's should be removed from the inputted raw p-value vector before further computation. Defaults to TRUE.

Value

An estimated null proportion:

pi0

A numeric value representing the proportion of the given data that come from the null distribution. A value in the interval [0,1].

Details

We run into errors or warnings when pvalues, zvalues, threshold or default.odds are not inputted correctly.

References

Rpack:bibtexRdpack

RFDRestimation

storey:2003FDRestimation

mein:2006FDRestimation

jiang:2008FDRestimation

nett:2006FDRestimation

pounds:2003FDRestimation

murray2020falseFDRestimation

See Also

plot.p.fdr, p.fdr, summary.p.fdr

Examples

# NOT RUN {
# Example 1
pi0 = 0.8
pi1 = 1-pi0
n = 10000
n.0 = ceiling(n*pi0)
n.1 = n-n.0

sim.data = c(rnorm(n.1,3,1),rnorm(n.0,0,1))
sim.data.p = 2*pnorm(-abs(sim.data))

get.pi0(sim.data.p, estim.method = "last.hist")
get.pi0(sim.data.p, estim.method = "storey")
get.pi0(sim.data.p, estim.method = "set.pi0")

# }