Adjusted p-Values for Bonferroni-Based Graphical Approaches

Obtains the adjusted p-values for graphical approaches using weighted Bonferroni tests.

fadjpbon(p, wgtmat = NULL)

Arguments

p

The raw p-values for elementary hypotheses.

wgtmat

A list containing the weight matrix and the indicator matrix for intersection hypotheses. If NULL, equal weights are assigned within each intersection hypothesis.

Value

A list with the following components:

• inthyp: The indicator matrix for the intersection hypotheses.

• pinter: The local p-values for the intersection hypotheses.

• padj: The adjusted p-values for the elementary hypotheses.

References

Frank Bretz, Willi Maurer, Werner Brannath and Martin Posch. A graphical approach to sequentially rejective multiple test procedures. Statistics in Medicine. 2009; 28:586-604.

Author

Kaifeng Lu, kaifenglu@gmail.com

Examples

pvalues <- matrix(c(0.01,0.005,0.015,0.022, 0.02,0.015,0.010,0.023),
                  nrow=2, ncol=4, byrow=TRUE)
w <- c(0.5,0.5,0,0)
G <- matrix(c(0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,0),
            nrow=4, ncol=4, byrow=TRUE)
wgtmat <- fwgtmat(w,G)
fadjpbon(pvalues, wgtmat)
#> $inthyp
#>       [,1] [,2] [,3] [,4]
#>  [1,]    1    1    1    1
#>  [2,]    1    1    1    0
#>  [3,]    1    1    0    1
#>  [4,]    1    1    0    0
#>  [5,]    1    0    1    1
#>  [6,]    1    0    1    0
#>  [7,]    1    0    0    1
#>  [8,]    1    0    0    0
#>  [9,]    0    1    1    1
#> [10,]    0    1    1    0
#> [11,]    0    1    0    1
#> [12,]    0    1    0    0
#> [13,]    0    0    1    1
#> [14,]    0    0    1    0
#> [15,]    0    0    0    1
#> 
#> $pinter
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] 0.01 0.01 0.01 0.01 0.02 0.01 0.02 0.01 0.01  0.01 0.005 0.005  0.03 0.015
#> [2,] 0.03 0.03 0.03 0.03 0.04 0.02 0.04 0.02 0.02  0.02 0.015 0.015  0.02 0.010
#>      [,15]
#> [1,] 0.022
#> [2,] 0.023
#> 
#> $padj
#>      [,1] [,2] [,3] [,4]
#> [1,] 0.02 0.01 0.03 0.03
#> [2,] 0.04 0.03 0.04 0.04
#>