MCRconfusion: Summary tables for MCRestimate objects
In MCRestimate: Misclassification error estimation with cross-validation

Description Usage Arguments Value Author(s) See Also Examples

MCRwrongsamples returns a matrix with all the samples that have a higher frequency of being predicted as a member of a wrong class than of the correct class for at least one classification method. MCRconfusion summarizes the result of the vote matrices

MCRwrongsamples(x,
         col.names=names(x),
         rownames.from.object=TRUE,
         subgroup=NULL,
         freq=FALSE)

MCRconfusion(x,
             col.names=names(x),
             row.names=NULL)

`x`	List of objects of S3 class `MCRestimate`
`col.names`	Vector of strings used for column names. The length must match the number of objects in `x`
`rownames.from.object`	Logical. If TRUE then the sample names of the `MCRestimate` object in `x` are used as row names
`subgroup`	Logical. If TRUE then only the samples which belongs to the specified group are listed in the table
`freq`	Logical. If TRUE then the frequency with which each sample in the table has been misclassified will be printed.
`row.names`	Vector of strings used for row names. If not specified the names of the groups are used

MCRwrongsamples returns a matrix and MCRconfusion returns a confusion matrix.

Markus Ruschhaupt mailto:m.ruschhaupt@dkfz.de

MCRestimate

library(golubEsets)
data(Golub_Train)
exSet <- Golub_Train[1:500,]
result1 <- MCRestimate(exSet,"ALL.AML",classification.fun="RF.wrap",cross.outer=3,cross.repeat=2)
result2 <- MCRestimate(exSet,"ALL.AML",classification.fun="PAM.wrap",poss.parameters=list(threshold=c(0.5,1)),cross.inner=3,cross.outer=3,cross.repeat=2)
MCRwrongsamples(list(result1,result2),subgroup="AML",col.names=c("Random Forest","PAM"))
MCRconfusion(list(result1,result2),col.names=c("Random Forest","PAM"))