vis.res {minet} | R Documentation

## Visualize Results

### Description

A group of functions to plot precision-recall and ROC curves and to compute f-scores from the data.frame returned by the `validate` function.

### Usage

      pr(table)
      rates(table)
      fscores(table, beta=1)
      show.pr(table,device=-1,...)
      show.roc(table,device=-1,...)

### Arguments

`table` | This is the (`steps` x 5) data.frame returned by the `validate` function where `steps` is the number of thresholds used in the validation process and where columns contain TP,FP,TN,FN values (confusion matrix) as well as the threshold value used - see `validate`.
`beta` | Numeric used as the weight of the recall in the f-score formula - see details. The default value of this argument is 1, meaning precision as important as recall.
`device` | The device to be used. This parameter allows the user to plot precision-recall and receiver operating characteristic curves for various inference algorithms on the same plotting window - see examples.
`...` | arguments passed to `plot`

### Details

A confusion matrix contains FP,TP,FN,FP values.
- "true positive rate" tpr = TP/(TN+TP)
- "false positive rate" fpr = FP/(FN+FP)
- "precision" p = TP/(FP+TP)
- "recall" r = TP/(TP+FN)
- "f-beta-score" Fbeta = (1+beta) * p*r/(r + beta*p)

### Value

The function `show.roc` (`show.pr`) plots the ROC-curve (PR-curve) and returns the device associated with the plotting window.
The function `pr` returns a (`steps` x 2) data.frame where steps is the number of thresholds used in the validation process. The first column contains precisions and the second recalls - see details.
The function `rates` also returns a (`steps` x 2) data.frame where the first column contains true positive rates and the second column false positive rates - see details.
The function `fscores` returns `steps` fscores according to the `steps` confusion matrices contained in the 'table' argument - see details.

### References

Patrick E. Meyer, Kevin Kontos, Frederic Lafitte, and Gianluca Bontempi. Information-theoretic inference of large transcriptional regulatory networks. EURASIP Journal on Bioinformatics and Systems Biology, 2007.

### See Also

`validate`, `plot`

### Examples

    data(syn.data)
    data(syn.net)
    # Inference
    mr <- minet( syn.data, method="mrnet", estimator="mi.empirical" )
    ar <- minet( syn.data, method="aracne", estimator="mi.empirical" )
    clr<- minet( syn.data, method="clr", estimator="mi.empirical" )
    # Validation
    mr.tbl <- validate(mr,syn.net)
    ar.tbl <- validate(ar,syn.net)
    clr.tbl<- validate(clr,syn.net)
    # Plot PR-Curves
    max(fscores(mr.tbl))
    dev <- show.pr(mr.tbl, col="green", type="b")
    dev <- show.pr(ar.tbl, device=dev, col="blue", type="b")
    show.pr(clr.tbl, device=dev, col="red",type="b")

[Package minet version 1.4.0 Index]