rowROC-class {genefilter} | 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
:"matrix"
The input data.ranks
:"matrix"
The ranked
input data. sens
:"matrix"
Matrix of
senitivity values for each gene at each cutpoint. spec
:"matrix"
Matrix of
specificity values for each gene at each cutpoint.pAUC
:"numeric"
The partial
area under the curve (integrated from 0 to p
. AUC
:"numeric"
The total area
under the curve. factor
:"factor"
The factor
used for classification.cutpoints
:"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
:"character"
The
names of the two classification cases.p
:"numeric"
The limit to which
pAUC
is integrated. signature(object="rowROC")
signature(x="rowROC", j="missing")
signature(x="rowROC", y="missing")
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")
0
to p
. This method
returns a new rowROC
object.signature(object="rowROC")
rowROC
object.signature(object="rowROC")
signature(object="rowROC")
signature(object="rowROC", total="logical")
Florian Hahne <f.hahne@dkfz.de>
Pepe MS, Longton G, Anderson GL, Schummer M.: Selecting differentially expressed genes from microarray experiments. Biometrics. 2003 Mar;59(1):133-42.
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) roc