compute Weights for each intersection Hypotheses in the closure of a graph based multiple testing procedure
Arguments
- g
Graph either defined as a matrix (each element defines how much of the local alpha reserved for the hypothesis corresponding to its row index is passed on to the hypothesis corresponding to its column index), as
graphMCPobject or asentangledMCPobject.- w
Vector of weights, defines how much of the overall alpha is initially reserved for each elementary hypothesis. Can be missing if
gis agraphMCPobject (in which case the weights from the graph object are used). Will be ignored ifgis anentangledMCPobject (since then the matrix of weights from this object is used).
Value
Returns matrix with each row corresponding to one intersection hypothesis in the closure of the multiple testing problem. The first half of elements indicate whether an elementary hypotheses is in the intersection (1) or not (0). The second half of each row gives the weights allocated to each elementary hypotheses in the intersection.
References
Bretz F, Maurer W, Brannath W, Posch M; (2008) - A graphical approach to sequentially rejective multiple testing procedures. - Stat Med - 28/4, 586-604 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
Author
Florian Klinglmueller <float@lefant.net>, Kornelius Rohmeyer rohmeyer@small-projects.de
Examples
g <- matrix(c(0,0,1,0,
0,0,0,1,
0,1,0,0,
1,0,0,0), nrow = 4,byrow=TRUE)
## Choose weights
w <- c(.5,.5,0,0)
## Weights of conventional gMCP test:
generateWeights(g,w)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
#> [1,] 0 0 0 1 0.0 0.0 0.0 1.0
#> [2,] 0 0 1 0 0.0 0.0 1.0 0.0
#> [3,] 0 0 1 1 0.0 0.0 0.5 0.5
#> [4,] 0 1 0 0 0.0 1.0 0.0 0.0
#> [5,] 0 1 0 1 0.0 1.0 0.0 0.0
#> [6,] 0 1 1 0 0.0 0.5 0.5 0.0
#> [7,] 0 1 1 1 0.0 0.5 0.5 0.0
#> [8,] 1 0 0 0 1.0 0.0 0.0 0.0
#> [9,] 1 0 0 1 0.5 0.0 0.0 0.5
#> [10,] 1 0 1 0 1.0 0.0 0.0 0.0
#> [11,] 1 0 1 1 0.5 0.0 0.0 0.5
#> [12,] 1 1 0 0 0.5 0.5 0.0 0.0
#> [13,] 1 1 0 1 0.5 0.5 0.0 0.0
#> [14,] 1 1 1 0 0.5 0.5 0.0 0.0
#> [15,] 1 1 1 1 0.5 0.5 0.0 0.0
g <- Entangled2Maurer2012()
generateWeights(g)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [1,] 0 0 0 0 1 0.0 0.0 0.0 0.0 1.0
#> [2,] 0 0 0 1 0 0.0 0.0 0.0 1.0 0.0
#> [3,] 0 0 0 1 1 0.0 0.0 0.0 0.5 0.5
#> [4,] 0 0 1 0 0 0.0 0.0 1.0 0.0 0.0
#> [5,] 0 0 1 0 1 0.0 0.0 1.0 0.0 0.0
#> [6,] 0 0 1 1 0 0.0 0.0 1.0 0.0 0.0
#> [7,] 0 0 1 1 1 0.0 0.0 1.0 0.0 0.0
#> [8,] 0 1 0 0 0 0.0 1.0 0.0 0.0 0.0
#> [9,] 0 1 0 0 1 0.0 1.0 0.0 0.0 0.0
#> [10,] 0 1 0 1 0 0.0 0.5 0.0 0.5 0.0
#> [11,] 0 1 0 1 1 0.0 0.5 0.0 0.5 0.0
#> [12,] 0 1 1 0 0 0.0 0.5 0.5 0.0 0.0
#> [13,] 0 1 1 0 1 0.0 0.5 0.5 0.0 0.0
#> [14,] 0 1 1 1 0 0.0 0.5 0.5 0.0 0.0
#> [15,] 0 1 1 1 1 0.0 0.5 0.5 0.0 0.0
#> [16,] 1 0 0 0 0 1.0 0.0 0.0 0.0 0.0
#> [17,] 1 0 0 0 1 0.5 0.0 0.0 0.0 0.5
#> [18,] 1 0 0 1 0 1.0 0.0 0.0 0.0 0.0
#> [19,] 1 0 0 1 1 0.5 0.0 0.0 0.0 0.5
#> [20,] 1 0 1 0 0 0.5 0.0 0.5 0.0 0.0
#> [21,] 1 0 1 0 1 0.5 0.0 0.5 0.0 0.0
#> [22,] 1 0 1 1 0 0.5 0.0 0.5 0.0 0.0
#> [23,] 1 0 1 1 1 0.5 0.0 0.5 0.0 0.0
#> [24,] 1 1 0 0 0 0.5 0.5 0.0 0.0 0.0
#> [25,] 1 1 0 0 1 0.5 0.5 0.0 0.0 0.0
#> [26,] 1 1 0 1 0 0.5 0.5 0.0 0.0 0.0
#> [27,] 1 1 0 1 1 0.5 0.5 0.0 0.0 0.0
#> [28,] 1 1 1 0 0 0.5 0.5 0.0 0.0 0.0
#> [29,] 1 1 1 0 1 0.5 0.5 0.0 0.0 0.0
#> [30,] 1 1 1 1 0 0.5 0.5 0.0 0.0 0.0
#> [31,] 1 1 1 1 1 0.5 0.5 0.0 0.0 0.0