MCRestimate(eset, class.column, reference.class=NULL, classification.fun, variableSel.fun="identity", cluster.fun="identity", poss.parameters=list(), cross.outer=10, cross.repeat=3, cross.inner=cross.outer, plot.label=NULL, rand=123, stratify=FALSE, information=TRUE, block.column=NULL, thePreprocessingMethods=c(variableSel.fun,cluster.fun))
ExpressionSet
MCRestimate
which is a list
with fourteen arguments:
If MCRestimate is used with an object of class
exprSetRG-class
, the preprocessing steps can use the
green and the red channel separately but the classification methods
works with green channel - red channel.
Note: 'correct prediction' means that a sample was predicted to be a member of the correct class at least as often as it was predicted to be a member of each other class. So in the two class problem a sample is also 'correct' if it has been predicted correctly half of the time.
library(golubEsets)
data(Golub_Test)
G2 <- Golub_Test[1:500,]
result <- MCRestimate(G2, "ALL.AML", classification.fun="RF.wrap",
cross.outer=4, cross.repeat=3)
result
if (interactive()) {
x11(width=9, height=4)}
plot(result)
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