plotROC-methods: plotROC

In transcriptR: An Integrative Tool for ChIP- And RNA-Seq Based Primary Transcripts Detection and Quantification

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)

transcriptR documentation built on Nov. 8, 2020, 8:12 p.m.