Fit Null Distribution To Censored Data by Maximum Likelihood
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).
censored.fit(x, cutoff, statistic=c("normal", "correlation", "pvalue", "studentt"))
fndr.cutoff(x, statistic=c("normal", "correlation", "pvalue", "studentt"))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. |
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.
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.
# 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")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.