gMCP(graph, pvalues, test, correlation, alpha=0.05,
approxEps=TRUE, eps=10^(-3), ..., useC=FALSE,
verbose=FALSE, keepWeights=TRUE, adjPValues=TRUE)graphMCP."Simes" the weighted Simes test will be performed for each sueps.TRUE neither adjusted p-values nor intermediate graphs are returned,
but the calculation is sped up by using code written in C. THIS CODE IS NOT FOR PRODUCTIVE USE YET!
If approxEps is FALSE and the graphTRUE verbose output is generated during
sequentially rejection steps.FALSE the weight of a node without outgoing edges is set to 0 if it is removed.
Otherwise it keeps its weight.FALSE no adjusted p-values will be calculated.
Especially for the weighted Simes test this will result in significantly less calculations in most cases.gMCPResult, more specifically a list with elementscorrelation where $\Phi^{-1}$ denotes the inverse of the standard
normal distribution function. For example, this is the case if $p_1,..., p_m$ are the raw p-values
from one-sided z-tests for each of the elementary hypotheses
where the correlation between z-test statistics is generated by
an overlap in the observations (e.g. comparison with a common
control, group-sequential analyses etc.). An application of the
transformation $\Phi^{-1}(1-p_i)$ to raw p-values from a
two-sided test will not in general lead to a multivariate normal
distribution. Partial knowledge of the correlation matrix is
supported. The correlation matrix has to be passed as a
numeric matrix with elements of the form: $correlation[i,i] =
1$ for diagonal elements, $correlation[i,j] = \rho_{ij}$, where
$\rho_{ij}$ is the known value of the correlation between
$\Phi^{-1}(1-p_i)$ and $\Phi^{-1}(1-p_j)$ or NA if
the corresponding correlation is unknown. For example
correlation[1,2]=0 indicates that the first and second test
statistic are uncorrelated, whereas correlation[2,3] = NA means
that the true correlation between statistics two and three is
unknown and may take values between -1 and 1. The correlation has
to be specified for complete blocks (ie.: if cor(i,j), and
cor(i,j') for i!=j!=j' are specified then cor(j,j') has to be
specified as well) otherwise the corresponding intersection null
hypotheses tests are not uniquely defined and an error is returned.
For further details see the given references.
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 - to appear.
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
graphNELg <- BonferroniHolm(5)
gMCP(g, pvalues=c(0.01, 0.02, 0.04, 0.04, 0.7))Run the code above in your browser using DataLab