Description Usage Arguments Value Note Author(s) References Examples
The two-stage estimator (TST) is one of a number of estimators of the proportion of true null hypotheses. It uses the Benjamini-Hochberg procedure at a reduced level to make the estimate. This implementation assumes a grouped structure for the data.
1 | c212.TST(trial.data, alpha)
|
trial.data |
Data frame containing the p-values for the hypotheses being tested. The data must contain the following columns: B: the index or name of the groupings; p: the p-values of the hypotheses. |
alpha |
The level for FDR control. E.g. 0.05. |
An estimate of the proportion of true null hypotheses.
The implementation is that described in Hu, J. X. and Zhao, H. and Zhou, H. H. (2010).
R. Carragher<raymond.carragher@strath.ac.uk>
Hu, J. X. and Zhao, H. and Zhou, H. H. (2010). False Discovery Rate Control With Groups. J Am Stat Assoc, 105(491):1215-1227.
Y. Benjamini, A. M. Krieger, and D. Yekutieli (2006). Adaptive linear step-up procedures that control the false discovery rate. Biometrika, 93(3):491–507.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | data(c212.FDR.data)
c212.TST(c212.FDR.data)
## Not run:
B pi0
1 Bdy-sys_5 1.0
2 Bdy-sys_6 1.0
3 Bdy-sys_7 1.0
4 Bdy-sys_8 1.0
5 Bdy-sys_2 0.5
6 Bdy-sys_3 0.0
7 Bdy-sys_4 1.0
8 Bdy-sys_1 1.0
## End(Not run)
|
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