gMCP (version 0.3-2)

Example Graphs: Functions that create different example graphs

Description

Functions that creates example graphs, e.g. graphs that represents a Bonferroni-Holm adjustment, parallel gatekeeping or special procedures from selected papers. We are providing functions and not the resulting graphs directly because this way you have additional examples: You can look at the function body with body and see how the graph is built.

Usage

createBonferroniHolmGraph(n, alpha=0.05)
	createGraphFromBretzEtAl(alpha=0.05)
	createGraphForParallelGatekeeping(alpha=0.05)
	createGraphForImprovedParallelGatekeeping(alpha=0.05)

Arguments

n

Number of hypotheses.

alpha

Overall type I error rate alpha.

Value

• A graph of class graphMCP that represents a sequentially rejective multiple test procedure.

Details

[object Object],[object Object],[object Object],[object Object]

References

Bonferroni, C. E.: Il calcolo delle assicurazioni su gruppi di teste. In Studi in Onore del Professore Salvatore Ortu Carboni. Rome: Italy, pp. 13-60, 1935.

Bonferroni, C. E.: Teoria statistica delle classi e calcolo delle probabilita. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze 8, 3-62, 1936.

Dmitrienko, A., Offen, W., Westfall, P.H. (2003). Gatekeeping strategies for clinical trials that do not require all primary effects to be significant. Statistics in Medicine. 22, 2387-2400.

Frank Bretz, Willi Maurer, Werner Brannath, Martin Posch: A graphical approach to sequentially rejective multiple test procedures. Statistics in Medicine 2009 vol. 28 issue 4 page 586-604. http://www.meduniwien.ac.at/fwf_adaptive/papers/bretz_2009_22.pdf

Hommel, G., Bretz, F. und Maurer, W. (2007) Powerful short-cuts for multiple testing procedures with special reference to gatekeeping strategies. Statistics in Medicine, 26(22), 4063-4073.

Examples

g <- createBonferroniHolmGraph(5)

# If Rgraphviz is installed, we can take a look at the graph:
library(Rgraphviz)
renderGraph(layoutGraph(g))

gMCP(g, pvalues=c(0.1, 0.2, 0.4, 0.4, 0.7))