Rejects a node/hypothesis and updates the graph accordingly.

## Arguments

- graph
A graph of class

`graphMCP`

or`entangledMCP`

.- node
A character string specifying the node to reject.

- upscale
Logical. If

`upscale=TRUE`

then the weights of all non-rejected nodes are scaled so that the sum is equal to 1. This forces`keepWeights=FALSE`

to reduce confusion, since otherwise the sum of weights could become bigger than 1.- verbose
Logical scalar. If

`TRUE`

verbose output is generated during sequentially rejection steps.- keepWeights
Logical scalar. If

`FALSE`

the weight of a node without outgoing edges is set to 0 if it is removed. Otherwise it keeps its weight.

## References

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

## Author

Kornelius Rohmeyer rohmeyer@small-projects.de

## Examples

```
m <- matrix(0, nrow = 4, ncol = 4)
m[1,3] <- m[2,4] <- m[3,2] <- m[4,1] <- 1
p1 <- c(0.01, 0.005, 0.01, 0.5)
a <- 0.05
w <- c(1/2, 1/2, 0, 0)
g <- matrix2graph(m, w)
gMCP(g, pvalues=p1, alpha=a)
#> gMCP-Result
#>
#> Initial graph:
#> A graphMCP graph
#> H1 (weight=0.5)
#> H2 (weight=0.5)
#> H3 (weight=0)
#> H4 (weight=0)
#> Edges:
#> H1 -( 1 )-> H3
#> H2 -( 1 )-> H4
#> H3 -( 1 )-> H2
#> H4 -( 1 )-> H1
#>
#>
#> P-values:
#> H1 H2 H3 H4
#> 0.010 0.005 0.010 0.500
#>
#> Adjusted p-values:
#> H1 H2 H3 H4
#> 0.02 0.01 0.02 0.50
#>
#> Alpha: 0.05
#>
#> Hypothesis rejected:
#> H1 H2 H3 H4
#> TRUE TRUE TRUE FALSE
#>
#> Final graph after3steps:
#> A graphMCP graph
#> H1 (rejected, weight=0)
#> H2 (rejected, weight=0)
#> H3 (rejected, weight=0)
#> H4 (weight=1)
#> No edges.
#>
rejectNode(graph = g, node = 4)
#> A graphMCP graph
#> H1 (weight=0.5)
#> H2 (weight=0.5)
#> H3 (weight=0)
#> H4 (rejected, weight=0)
#> Edges:
#> H1 -( 1 )-> H3
#> H2 -( 1 )-> H1
#> H3 -( 1 )-> H2
#>
```