censored.fit: Fit Null Distribution To Censored Data by Maximum Likelihood

Description

censored.fit fits a null distribution to censored data.

fndr.cutoff finds a suitable cutoff point based on the (approximate) false non-discovery rate (FNDR).

Usage

censored.fit(x, cutoff, statistic=c("normal", "correlation", "pvalue", "studentt"))
fndr.cutoff(x, statistic=c("normal", "correlation", "pvalue", "studentt"))

Value

censored.fit returns a matrix whose rows contain the estimated parameters and corresponding errors for each cutoff point.

fndr.cutoff returns a tentative cutoff point.

Arguments

x: vector of test statistics.
cutoff: truncation point (this may a single value or a vector).
statistic: type of statistic - normal, correlation, or student t.

Details

As null model truncated normal, truncated student t or a truncated correlation density is assumed. The truncation point is specified by the cutoff parameter. All data points whose absolute value are large than the cutoff point are ignored when fitting the truncated null model via maximum likelihood. The total number of data points is only used to estimate the fraction of null values eta0.

Examples

Run this code

# load "fdrtool" library
library("fdrtool")

# simulate normal data
sd.true = 2.232
n = 5000
z = rnorm(n, sd=sd.true)
censored.fit(z, c(2,3,5), statistic="normal")


# simulate contaminated mixture of correlation distribution
r = rcor0(700, kappa=10)
u1 = runif(200, min=-1, max=-0.7)
u2 = runif(200, min=0.7, max=1)
rc = c(r, u1, u2)

censored.fit(r, 0.7, statistic="correlation")
censored.fit(rc, 0.7, statistic="correlation")

# pvalue example
data(pvalues)
co = fndr.cutoff(pvalues, statistic="pvalue")
co
censored.fit(pvalues, cutoff=co, statistic="pvalue")

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