A class to model ROC curves and corresponding area under the curve as produced by rowpAUCs.

Objects can be created by calls of the form `new("rowROC", ...)`

.

`data`

:Object of class

`"matrix"`

The input data.`ranks`

:Object of class

`"matrix"`

The ranked input data.`sens`

:Object of class

`"matrix"`

Matrix of senitivity values for each gene at each cutpoint.`spec`

:Object of class

`"matrix"`

Matrix of specificity values for each gene at each cutpoint.`pAUC`

:Object of class

`"numeric"`

The partial area under the curve (integrated from 0 to`p`

.`AUC`

:Object of class

`"numeric"`

The total area under the curve.`factor`

:Object of class

`"factor"`

The factor used for classification.`cutpoints`

:Object of class

`"matrix"`

The values of the cutpoints at which specificity ans sensitivity was calculated. (Note: the data is ranked prior to computation of ROC curves, the cutpoints map to the ranked data.`caseNames`

:Object of class

`"character"`

The names of the two classification cases.`p`

:Object of class

`"numeric"`

The limit to which`pAUC`

is integrated.

- show
`signature(object="rowROC")`

Print nice info about the object.

- [
`signature(x="rowROC", j="missing")`

Subset the object according to rows/genes.

- plot
`signature(x="rowROC", y="missing")`

Plot the ROC curve of the first row of the object along with the

`pAUC`

. To plot the curve for a specific row/gene subsetting should be done first (i.e.`plot(rowROC[1])`

.- pAUC
`signature(object="rowROC", p="numeric", flip="logical")`

Integrate area under the curve from

`0`

to`p`

. This method returns a new`rowROC`

object.- AUC
`signature(object="rowROC")`

Integrate total area under the curve. This method returns a new

`rowROC`

object.- sens
`signature(object="rowROC")`

Accessor method for sensitivity slot.

- spec
`signature(object="rowROC")`

Accessor method for specificity slot.

- area
`signature(object="rowROC", total="logical")`

Accessor method for pAUC slot.

Florian Hahne <fhahne@fhcrc.org>

Pepe MS, Longton G, Anderson GL,
Schummer M.: Selecting
differentially expressed genes from microarray
experiments. *Biometrics. 2003 Mar;59(1):133-42.*

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