| c212.BH {c212} | R Documentation |
Implementation of Benjamini-Hochberg procedure for False Discovery Rate control
Description
Implementaion of Benjamini-Hochberg procedure for False Discovery Rate control. The hypotheses' data can be contained in a file or data frame.
Usage
c212.BH(trial.data, alpha = 0.05)
Arguments
trial.data |
File or data frame containing the p-values for the hypotheses being tested. The data must include a column called p which contains the p-values of the hypotheses. |
alpha |
The level for FDR control. E.g. 0.05. |
Value
The subset of hypotheses in file or trial.data deemed significant by the Benjamini-Hochberg procedure.
Note
No check is made for duplicate rows in the input file or data frame.
Author(s)
R. Carragher<raymond.carragher@strath.ac.uk>
References
Benjamini, Yoav and Hochberg, Yosef, (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1):289-300.
Examples
trial.data <- data.frame(B = c(1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4),
j = c(1, 1, 2, 3, 4, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5),
AE = c("AE1", "AE2", "AE3", "AE4", "AE5", "AE6", "AE7", "AE8", "AE9", "AE10", "AE11",
"AE12", "AE13", "AE14", "AE15", "AE16", "AE17"),
p = c(0.135005, 0.010000, 0.001000, 0.005000, 0.153501, 0.020000, 0.0013, 0.0023,
0.011, 0.023000, 0.016, 0.0109, 0.559111, 0.751986, 0.308339, 0.837154, 0.325882))
c212.BH(trial.data, 0.05)
## Not run:
B j AE p
1 2 2 AE3 0.0010
2 3 2 AE7 0.0013
3 3 3 AE8 0.0023
4 2 3 AE4 0.0050
5 2 1 AE2 0.0100
6 3 7 AE12 0.0109
7 3 4 AE9 0.0110
8 3 6 AE11 0.0160
9 3 1 AE6 0.0200
10 3 5 AE10 0.0230
## End(Not run)