Description Usage Arguments Value Author(s) References See Also Examples
Convenience function to generate a ROC curve from several runs and iterations for one model or a selection of models contained in a mc.acc
object (see details mc.agg
). This function allows the averaging of several ROC curves produced on each data partitioning of the resampling strategy (i.e crossvalidation runs repeated or not several times). Three methods are available to perform the averaging: "horiz" (horizontal), "vert" (vertical) and "thres" (thresholding). (see reference for further details). The default value ("all") means that each data points from individual ROC curves are strictly concatenated into a single ROC curve.
1 2 3 
mc.obj 

lmod 
List of models to be considered  Default: all of them 
method 
Aggregation method ("all", "thres", "vert", "horiz") 
roc.list
object is a list of two components:
roc 
List of ROC curves of length equal to the number of models. For each curve true positive (tpr), false positive rate (fpr), decision boundary threshold (thres) and type (type) of ROC aggregation are given. 
cldef 
Identical to 
David Enot [email protected]
Fawcett, T. (2004). ROC graphs: notes and practical considerations for researchers.
Technical Report HPL20034
1 2 3 4 5 6 7 8 9 10 11 12 13 14  data(iris)
dat=as.matrix(iris[,1:4])
cl=as.factor(iris[,5])
lrnd=sample(1:150)[1:50]
cl[lrnd]=sample(cl[lrnd])
pars < valipars(sampling = "cv",niter = 2, nreps=10)
dat1=dat.sel1(dat,cl,pwise="virginica",mclass=NULL,pars=pars)
res1=lapply(dat1,function(x) accest(x,clmeth="lda"))
res2=lapply(dat1,function(x) accest(x,clmeth="randomForest",ntree=50))
mc=mc.agg(res1,res2)
roc.sv=mc.roc(mc,lmod=1:4,method="thres")
print(roc.sv)

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