net.infer.ST: Inferring functionally related proteins with self training

Description Usage Arguments Value Author(s) See Also Examples

View source: R/net.infer.ST.R

Description

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.

Usage

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net.infer.ST(target, kernel, top = NULL, C1 = 1, nu = 0.2,
            epsilon = 0.1, cache1 = 40, tol1 = 0.001, shrinking1 = TRUE,
            C2 = 1, cache2 = 40, tol2 = 0.001, shrinking2 = TRUE,
            thrConf = 0.9, maxIts = 10, percFull = 1, verbose = FALSE) 

Arguments

target

<<<<<<< HEAD set of interesting proteins or target class ======= list of interesting proteins or target class >>>>>>> upstream/RELEASE_3_5

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)

C1

cost of constraints violation for OCSVM (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)

C2

cost of constraints violation (default: 1) for SVM

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)

Value

list

list of a target class used in the model

error

training error

top

top proteins

score

decision values for top proteins

Author(s)

Dongmin Jung, Xijin Ge

See Also

self.train

Examples

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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)

PPInfer documentation built on May 2, 2018, 3:11 a.m.