powerRandomized: Power Calculation for Completely Randomized Treatment-Control...

In sizepower: Sample Size and Power Calculation in Micorarray Studies

Description

This routine computes the individual power value for a completely randomized design with n treatment units and n control units (2n units in total). This power value is the expected fraction of truly differentially expressed genes that will be correctly declared as differentially expressed by the tests.

Usage

  power.randomized(ER0, G0, absMu1, sigmad, n)

Arguments

ER0	mean number of false positives.
G0	anticipated number of genes in the experiment that are not differentially expressed.
absMu1	absolute mean difference in log-expression between treatment and control conditions as postulated under the alternative hypothesis H1.
sigmad	anticipated standard deviation of the difference in log-expression between treatment and control conditions. The relation between the standard deviation of the difference (sigmad) and the experimental error standard deviation (sigma) is sigmad=sqrt(2)/sigma.
n	the sample size for each group.

Value

power	power.
psi1	non-centrality parameter.

Note

Examples and explainations can be found in http://www.biostat.harvard.edu/people/faculty/mltlee/pdf/Web-power-trt-cont050510.pdf.

Author(s)

Weiliang Qiu ([e-mail]), Mei-Ling Ting Lee ([e-mail]), George Alex Whitmore ([e-mail])

References

Lee, M.-L. T. (2004). Analysis of Microarray Gene Expression Data. Kluwer Academic Publishers, ISBN 0-7923-7087-2.

Lee, M.-L. T., Whitmore, G. A. (2002). Power and sample size for DNA microarray studies. Statistics in Medicine, 21:3543-3570.

See Also

power.matched, power.multi, sampleSize.randomized, sampleSize.matched

Examples

  power.randomized(ER0=2, G0=5000, absMu1=1, sigmad=0.5657, n=8)

sizepower documentation built on Nov. 8, 2020, 5:26 p.m.