Function to execute multiple cross-validation with random walk based, labelprop and GBA methods

1 | ```
multiple.RW.cv(W, ind.pos, k = 5, p = 100, init.seed = 0, fun = RW, ...)
``` |

`W` |
a numeric matrix representing the adjacency matrix of the graph. Note that if the optional argument norm=TRUE (def.), the W matrix is normalized, otherwise it is assumed that W is just normalized |

`ind.pos` |
indices of the "core" positive examples of the graph. They represent the indices of W corresponding to the positive examples |

`k` |
number of folds (def: 5) |

`p` |
number of repeated cross-validations |

`init.seed` |
initial seed for the random generator. If 0 (default) no initialization is performed |

`fun` |
function. It must be one of the following functions: - RW (default) - RWR - label.prop - GBAsum - GBAmax |

`...` |
optional arguments for the function fun: - gamma : restart parameter (def: 0.6) (meaningful only for RWR) - tmax : maximum number of iterations (def: 1000) - eps : maximum allowed difference between the computed probabilities at the steady state (def. 1e-10) |

Function to execute multiple cross-validation with random walk based, labelprop and GBA methods for a single class. It computes the scores by averaging across multiple cross validations. It can be used with of the following methods: RW, RWR, label.prop, GBAsum, GBAmax.

a vector with the the probabilities for each example at the steady state averaged across multiple cross-validations

`RW`

, `RWR`

, `label.prop`

, `GBAsum`

, `GBAmax`

, `RW.cv`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
# Nodel label ranking of the DrugBank category Penicillins
# on the Tanimoto chemical structure similarity network (1253 drugs)
# using 5 fold cross-validation repeated 2 times
# and "vanilla" 2-step random walk
library(bionetdata);
data(DD.chem.data);
data(DrugBank.Cat);
labels <- DrugBank.Cat[,"Penicillins"];
ind.pos <- which(labels==1);
res <- multiple.RW.cv(DD.chem.data, ind.pos, k = 5, p = 2, init.seed = 0, fun = GBAmax)
## Not run:
# the same but using the label.prop
res <- multiple.RW.cv(DD.chem.data, ind.pos, k = 5, p = 2, init.seed = 0, fun = label.prop, tmax=2)
# the same but using "vanilla" 2-step random walk
res <- multiple.RW.cv(DD.chem.data, ind.pos, k = 5, p = 2, init.seed = 0, fun = RW, tmax=2)
## End(Not run)
``` |

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