net.infer.ST {PPInfer} | R Documentation |
This function is the self-training version of net.infer. The function net.infer is the special case of net.infer.ST where a single iteration is conducted.
net.infer.ST(target, kernel, top = NULL, C = 1, nu = 0.2, epsilon = 0.1, cache1 = 40, tol1 = 0.001, shrinking1 = TRUE, cache2 = 40, tol2 = 0.001, shrinking2 = TRUE, thrConf = 0.9, maxIts = 10, percFull = 1, verbose = FALSE)
target |
set of interesting proteins or target class |
kernel |
the regularized Laplacian matrix for a graph |
top |
number of top proteins most closely related to target class (default: all proteins except for target and pseudo-absence class) |
C |
cost of constraints violation for SVM (default: 1) |
nu |
The nu parameter for OCSVM (default: 0.2) |
epsilon |
epsilon in the insensitive-loss function for OCSVM (default: 0.1) |
cache1 |
cache memory in MB for OCSVM (default: 40) |
tol1 |
tolerance of termination criterion for OCSVM (default: 0.001) |
shrinking1 |
option whether to use the shrinking-heuristics for OCSVM (default: TRUE) |
cache2 |
cache memory in MB for SVM (default: 40) |
tol2 |
tolerance of termination criterion for SVM (default: 0.001) |
shrinking2 |
option whether to use the shrinking-heuristics for SVM (default: TRUE) |
thrConf |
A number between 0 and 1, indicating the required classification confidence for an unlabelled case to be added to the labelled data set with the label predicted predicted by the classification algorithm (default: 0.9) |
maxIts |
The maximum number of iterations of the self-training process (default: 10) |
percFull |
A number between 0 and 1. If the percentage of labelled cases reaches this value the self-training process is stoped (default: 1) |
verbose |
A boolean indicating the verbosity level of the function. (default: FALSE) |
list |
list of a target class used in the model |
error |
training error |
top |
top proteins |
score |
decision values for top proteins |
Dongmin Jung, Xijin Ge
self.train
data(litG) litG <- igraph.from.graphNEL(litG) sg <- decompose(litG, min.vertices = 50) sg <- sg[[1]] K <- net.kernel(sg) litG.infer.ST <- net.infer.ST(names(V(sg))[1:10], K, top=20)