plotROC {transcriptR} | R Documentation

## plotROC

### Description

Visualize the performance of the classification model fit (prediction of the gene associated peaks).

### Usage

    plotROC(object, ...)

    ## S4 method for signature 'ChipDataSet'
    plotROC(object, ...)

### Arguments

`object` | A `ChipDataSet` object.
`...` | Further arguments passed to plot.

### Details

The `plotROC` is a simple wrapper for the `plot` function implemented in `pROC` package.

The ROC curve is created by plotting the true positive rate (sensitivity) against the false positive rate (1 - specificity) at various threshold settings. The closer the curve follows the left-hand border and then the top border of the ROC space, the more accurate the test. The area under the curve (AUC) is a measure of accuracy.

### Value

ROC plot.

### Author(s)

Armen R. Karapetyan

### See Also

`predictTssOverlap`

### Examples

    ### Load ChipDataSet object
    data(cds)

    ### Classify peaks on gene associated and background
    predictTssOverlap(object = cds, feature = "pileup", p = 0.75)

    ### Visualize the performance of the model fit
    plotROC(object = cds, col = "red3", grid = TRUE, auc.polygon = TRUE)

[Package transcriptR version 1.12.0 Index]