View source: R/gMCP.extended.R
gMCP.extended | R Documentation |
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,
...
)
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:
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. 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. \Sexpr[results=rd]{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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