fdrtool {fdrtool}

Estimate (Local) False Discovery Rates For Diverse Test Statistics

Description

fdrtool takes a vector of z-scores (or of correlations, p-values, or t-statistics), and estimates for each case both the tail area-based Fdr as well as the density-based fdr (=q-value resp. local false discovery rate). The parameters of the null distribution are estimated adaptively from the data (except for the case of p-values where this is not necessary).

Usage

fdrtool(x, statistic=c("normal", "correlation", "pvalue"),
  plot=TRUE, color.figure=TRUE, verbose=TRUE, 
  cutoff.method=c("fndr", "pct0", "locfdr"),
  pct0=0.75)

Arguments

x

vector of the observed test statistics.

statistic

one of "normal" (default), "correlation", "pvalue". This species the null model.

plot

plot a figure with estimated densities, distribution functions, and (local) false discovery rates.

verbose

print out status messages.

cutoff.method

one of "fndr" (default), "pct0", "locfdr".

pct0

fraction of data used for fitting null model - only if cutoff.method="pct0"

color.figure

determines whether a color figure or a black and white figure is produced (defaults to "TRUE", i.e. to color figure).

Details

The algorithm implemented in this function proceeds as follows:

1. A suitable cutoff point is determined. If cutoff.method is "fndr" then first an approximate null model is fitted and subsequently a cutoff point is sought with false nondiscovery rate as small as possible (see fndr.cutoff). If cutoff.method is "pct0" then a specified quantile (default value: 0.75) of the data is used as the cutoff point. If cutoff.method equals "locfdr" then the heuristic of the "locfdr" package (version 1.1-6) is employed to find the cutoff (z-scores and correlations only).

2. The parameters of the null model are estimated from the data using censored.fit. This results in estimates for scale parameters und and proportion of null values (eta0).

3. Subsequently the corresponding p-values are computed, and a modified grenander algorithm is employed to obtain the overall density and distribution function (note that this respects the estimated eta0).

4. Finally, q-values and local fdr values are computed for each case.

The assumed null models all have (except for p-values) one free scale parameter. Note that the z-scores and the correlations are assumed to have zero mean.

Value

A list with the following components:

pval

a vector with p-values for each case.

qval

a vector with q-values (Fdr) for each case.

lfdr

a vector with local fdr values for each case.

statistic

the specified type of null model.

param

a vector containing the estimated parameters (the null proportion eta0 and the free parameter of the null model).

Author(s)

Korbinian Strimmer (https://strimmerlab.github.io).

References

Strimmer, K. (2008a). A unified approach to false discovery rate estimation. BMC Bioinformatics 9: 303. <DOI:10.1186/1471-2105-9-303>

Strimmer, K. (2008b). fdrtool: a versatile R package for estimating local and tail area- based false discovery rates. Bioinformatics 24: 1461-1462. <DOI:10.1093/bioinformatics/btn209>

See Also

pval.estimate.eta0, censored.fit.

Examples

# load "fdrtool" library and p-values
library("fdrtool")
data(pvalues)


# estimate fdr and Fdr from p-values

data(pvalues)
fdr = fdrtool(pvalues, statistic="pvalue")
fdr$qval # estimated Fdr values 
fdr$lfdr # estimated local fdr 

# the same but with black and white figure  
fdr = fdrtool(pvalues, statistic="pvalue", color.figure=FALSE)


# estimate fdr and Fdr from z-scores

sd.true = 2.232
n = 500
z = rnorm(n, sd=sd.true)
z = c(z, runif(30, 5, 10)) # add some contamination
fdr = fdrtool(z)

# you may change some parameters of the underlying functions
fdr = fdrtool(z, cutoff.method="pct0", pct0=0.9)