Performs a graph based multiple test procedure for a given graph and unadjusted p-values.

1 2 | ```
gMCP.extended(graph, pvalues, test, alpha = 0.05, eps = 10^(-3),
upscale = FALSE, verbose = FALSE, adjPValues = TRUE, ...)
``` |

`graph` |
A graph of class |

`pvalues` |
A numeric vector specifying the p-values for the graph based
MCP. Note the assumptions in the description of the selected test (if there are any -
for example |

`test` |
A weighted test function. The package gMCP provides the following weighted test functions: - bonferroni.test
Bonferroni test - see `?bonferroni.test` for details.- parametric.test
Parametric test - see `?parametric.test` for details.- simes.test
Simes test - see `?simes.test` for details.- bonferroni.trimmed.simes.test
Trimmed Simes test for intersections of two hypotheses and otherwise Bonferroni - see `?bonferroni.trimmed.simes.test` for details.- simes.on.subsets.test
Simes test for intersections of hypotheses from certain sets and otherwise Bonferroni - see `?simes.on.subsets.test` for details.
To provide your own test function see |

`alpha` |
A numeric specifying the maximal allowed type one error rate. |

`eps` |
A numeric scalar specifying a value for epsilon edges. |

`upscale` |
Logical. If |

`verbose` |
Logical scalar. If |

`adjPValues` |
Logical scalar. If |

`...` |
Test specific arguments can be given here. |

An object of class `gMCPResult`

, more specifically a list with
elements

`graphs`

list of graphs

`pvalues`

p-values

`rejected`

logical whether hyptheses could be rejected

`adjPValues`

adjusted p-values

Kornelius Rohmeyer rohmeyer@small-projects.de

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

Bretz F., Posch M., Glimm E., Klinglmueller F., Maurer W., Rohmeyer K. (2011): Graphical approaches for multiple endpoint problems using weighted Bonferroni, Simes or parametric tests. Biometrical Journal 53 (6), pages 894-913, Wiley. http://onlinelibrary.wiley.com/doi/10.1002/bimj.201000239/full

Strassburger K., Bretz F.: Compatible simultaneous lower confidence bounds for the Holm procedure and other Bonferroni based closed tests. Statistics in Medicine 2008; 27:4914-4927.

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

Guilbaud O.: Simultaneous confidence regions corresponding to Holm's stepdown procedure and other closed-testing procedures. Biometrical Journal 2008; 50:678-692.

`graphMCP`

`graphNEL`

1 2 3 4 5 6 7 8 9 10 11 | ```
g <- BonferroniHolm(5)
gMCP(g, pvalues=c(0.01, 0.02, 0.04, 0.04, 0.7))
# Simple Bonferroni with empty graph:
g2 <- matrix2graph(matrix(0, nrow=5, ncol=5))
gMCP(g2, pvalues=c(0.01, 0.02, 0.04, 0.04, 0.7))
# With 'upscale=TRUE' equal to BonferroniHolm:
gMCP(g2, pvalues=c(0.01, 0.02, 0.04, 0.04, 0.7), upscale=TRUE)
# Entangled graphs:
g3 <- Entangled2Maurer2012()
gMCP(g3, pvalues=c(0.01, 0.02, 0.04, 0.04, 0.7), correlation=diag(5))
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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