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Performs a graph based multiple test procedure for a given graph and unadjusted p-values.
gMCP.extended(
graph,
pvalues,
test,
alpha = 0.05,
eps = 10^(-3),
upscale = FALSE,
verbose = FALSE,
adjPValues = TRUE,
...
)An object of class gMCPResult, more specifically a list with
elements
graphslist of graphs
pvaluesp-values
rejectedlogical whether hyptheses could be rejected
adjPValuesadjusted p-values
A graph of class graphMCP.
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=bonferroni.test has no further assumptions, but
test=parametric.test assumes p-values from a multivariate normal distribution).
A weighted test function.
The package gMCP provides the following weighted test functions:
Bonferroni test - see ?bonferroni.test for details.
Parametric test - see ?parametric.test for details.
Simes test - see ?simes.test for details.
Trimmed Simes test for intersections of two hypotheses and otherwise Bonferroni - see ?bonferroni.trimmed.simes.test for details.
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 ?weighted.test.function.
A numeric specifying the maximal allowed type one error rate.
A numeric scalar specifying a value for epsilon edges.
Logical. If upscale=FALSE then for each intersection
of hypotheses (i.e. each subgraph) a weighted test is performed at the
possibly reduced level alpha of sum(w)*alpha,
where sum(w) is the sum of all node weights in this subset.
If upscale=TRUE all weights are upscaled, so that sum(w)=1.
Logical scalar. If TRUE verbose output is generated
during sequentially rejection steps.
Logical scalar. If FALSE no adjusted p-values will
be calculated. Especially for the weighted Simes test this will result in
significantly less calculations in most cases.
Test specific arguments can be given here.
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. https://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. tools:::Rd_expr_doi("10.1002/bimj.201000239")
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
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))
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