multiple.RW.cv | R Documentation |
Function to execute multiple cross-validation with random walk based, labelprop and GBA methods
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
# 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) # 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)
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