Class "rowROC"
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.
library(Biobase)
require(genefilter)
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)
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