validate {minet}R Documentation

Inference Validation

Description

validate compares the infered network to the true underlying network for several threshold values and appends the resulting confusion matrices to the returned object.

Usage

validate( inet, tnet, steps=50 )

Arguments

inet This is the infered network, a data.frame or matrix obtained by one of the functions minet, aracne, clr or mrnet .
tnet The true underlying network. This network must have the same size and variable names as inet.
steps The number of threshold values to be used in the validation process - see details.

Details

For each of the steps threshold values T, the edges whose weight are (strictly) below T are eliminated. All the other edges will have a weight 1. Thus for each threshold, we obtain a boolean network from the infered network. This network is compared to the true underlying network, tnet, in order to compute a confusion (adjacency) matrix. All the confusion matrices, obtained with different threshold values, are appended to the returned object. In the end the validate function returns a data.frame containing steps confusion matrices.

Value

validate returns a data.frame whith four columns named thrsh, tp, fp, fn. These values are computed for each of the steps thresholds. Thus each row of the returned object contains the confusion matrix for a different threshold.

See Also

minet, vis.res

Examples

data(syn.data)
data(syn.net)
inf.net <- mrnet(build.mim(discretize(syn.data)))
table <- validate( inf.net, syn.net, steps=100 )

[Package minet version 1.2.0 Index]