look only at a fraction alpha0 of the p-values (default: 1, i.e. all p-values).
plot
show plot with HC decision threshold.
Details
Higher Criticism (HC) provides an alternative means to determine decision thresholds for
signal identification, especially if the signal is rare and weak.
See Donoho and Jin (2008) for details of this approach
and Klaus and Strimmer (2012) for a review and connections with FDR methdology.
Value
hc.score returns a vector with the HC score corresponding to each p-value.
hc.thresh returns the p-value corresponding to the maximum HC score.
Donoho, D. and J. Jin. (2008). Higher criticism thresholding: optimal feature selection
when useful features are rare and weak. Proc. Natl. Acad. Sci. USA 105:14790-15795.
Klaus, B., and K. Strimmer (2013). Signal identification for rare and
weak features: higher criticism or false discovery rates?
Biostatistics 14: 129-143. Preprint available from http://arxiv.org/abs/1112.2615.
See Also
fdrtool.
Examples
# load "fdrtool" library
library("fdrtool")
# some p-values
data(pvalues)
# compute HC scores
hc.score(pvalues)
# determine HC threshold
hc.thresh(pvalues)