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]