multiple.ker.score.cv | R Documentation |
Function to execute multiple cross-validation with RANKS for a single class.
multiple.ker.score.cv(RW, ind.pos, m = 5, p = 100, stratified=TRUE, init.seed = 0, fun = KNN.score, ...)
RW |
matrix. Kernel matrix or any valid symmetric matrix |
ind.pos |
indices of the positive examples. They are the row indices of RW corresponding to positive examples. |
m |
number of folds for each cross-validation |
p |
number of repeated cross-validations |
stratified |
boolean. If TRUE (def.) stratified CV is performed otherwise vanilla CV is done |
init.seed |
initial seed for the random generator (def: 0) |
fun |
function. It must be a kernel-based score method (default KNN.score) |
... |
optional arguments for the function fun |
It performs multiple cross-validation using RANKS to predict the cross-validated scores. The cross-validation is stratified: the folds are constructed separately for each class, to maintain an equal ratio between classes among folds. It computes the scores by averaging across multiple cross validations.
A list with two components:
av.scores |
a vector with the average scores across multiple cross-validations. Elements of the vector av.scores correspond to the rows of RW |
pos.scores |
a vector with the scores of positive elements collected at each iteration |
ker.score.cv
, multiple.ker.score.thresh.cv
, rw.kernel-methods
, Kernel functions
.
# Nodel label ranking for the DrugBank category Penicillins # on the Tanimoto chemical structure similarity network (1253 drugs) # using 5 fold cross-validation repeated 10 times # and eav-score with 1-step random walk kernel library(bionetdata); data(DD.chem.data); data(DrugBank.Cat); labels <- DrugBank.Cat[,"Penicillins"]; ind.pos <- which(labels==1); K <- rw.kernel(DD.chem.data); res <- multiple.ker.score.cv(K, ind.pos, m = 5, p = 10, init.seed = 0, fun = eav.score); # the same but using the NN-score res <- multiple.ker.score.cv(K, ind.pos, m = 5, p = 10, init.seed = 0, fun = NN.score);
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