rowROC-class | R Documentation |
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.
signature(object="rowROC")
Print nice info about the object.
signature(x="rowROC", j="missing")
Subset the object according to rows/genes.
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])
.
signature(object="rowROC", p="numeric", flip="logical")
Integrate
area under the curve from 0
to p
. This method
returns a new rowROC
object.
signature(object="rowROC")
Integrate
total area under the curve. This method returns a new
rowROC
object.
signature(object="rowROC")
Accessor method for sensitivity slot.
signature(object="rowROC")
Accessor method for specificity slot.
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.
rowpAUCs
library("Biobase")
data("sample.ExpressionSet")
roc <- rowpAUCs(sample.ExpressionSet, "sex", p=0.5)
roc
area(roc[1:3])
if(interactive()) {
par(ask=TRUE)
plot(roc)
plot(1-spec(roc[1]), sens(roc[2]))
par(ask=FALSE)
}
pAUC(roc, 0.1)
roc
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