Description Usage Arguments Details Value Note Author(s) References See Also Examples

Rank nodes in an object of class `igraph`

(see package
`igraph`

for the definition of class `igraph`

) containing a
minimum spanning tree (MST) according to the High Directed Preorder traversal
of the tree.

1 | ```
HDP.ranking(object)
``` |

`object` |
object of class |

Rank nodes in an object of class `igraph`

(see package
`igraph`

) containing a minimum spanning tree (MST). The MST is
rooted at a node with the largest geodesic distance and the rest of the nodes
are ranked according to the high directed preorder (HDP) traversal of the tree
(Friedman and Rafsky 1979).

Numeric vector giving the node ranks according to HDP traversal of the MST.

This function does not work properly if there is any node in the MST with more than 26 links. However, this situation is almost impossible for a dataset composed of a few hundreds or less of samples.

Yasir Rahmatallah and Galina Glazko

Rahmatallah Y., Emmert-Streib F. and Glazko G. (2012) Gene set analysis for
self-contained tests: complex null and specific alternative hypotheses.
Bioinformatics **28**, 3073–3080.

Friedman J. and Rafsky L. (1979) Multivariate generalization of the
Wald-Wolfowitz and Smirnov two-sample tests. Ann. Stat. **7**, 697–717.

`radial.ranking`

, `KStest`

, `MDtest`

.

1 2 3 4 5 6 7 8 9 10 11 | ```
## generate random data using normal distribution
## generate 20 features in 20 samples
object <- matrix(rnorm(400),20,20)
objt <- aperm(object, c(2,1))
## calculate the weight matrix
Wmat <- as.matrix(dist(objt, method = "euclidean", diag = TRUE, upper = TRUE, p = 2))
## create a weighted undirectional graph from the weight matrix
gr <- graph_from_adjacency_matrix(Wmat, weighted = TRUE, mode = "undirected")
## find the minimum spanning tree
MST <- mst(gr)
HDP.ranks <- HDP.ranking(MST)
``` |

GSAR documentation built on May 2, 2018, 2:35 a.m.

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