validate {minet} | R Documentation |
validate
compares the infered network to the true underlying network for several threshold values
and appends the resulting confusion matrices to the returned object.
validate( inet, tnet, steps=50 )
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 . |
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.
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.
data(syn.data) data(syn.net) inf.net <- mrnet(build.mim(discretize(syn.data))) table <- validate( inf.net, syn.net, steps=100 )