MCRestimate: Estimation of misclassification error by cross-validation
In MCRestimate: Misclassification error estimation with cross-validation

Description Usage Arguments Details Value Author(s) Examples

Several repetitions of a cross-validation are performed to get 'votes' how stable a method is against different partitions into training and test set

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))

`eset`	an object of class `ExpressionSet`
`class.column`	a number or a character string which indicates the column of the expression set's phenodata containing the class label
`reference.class`	a character string - the name of one class - if specified, the class will form the first class and all the other classes will form the second class
`classification.fun`	character string which names the function that should be used for the classification
`variableSel.fun`	character string which names the function that should be used for the variable selection
`cluster.fun`	character string which names the function that should be used for the clustering of variables
`thePreprocessingMethods`	vector of character with the names of all preprocessing functions - can be used instead of 'variableSel.fun' and 'cluster.fun' - see details
`poss.parameters`	a list of possible values for the parameter of the classification, variable selection, and cluster methods
`cross.outer`	integer - the number of nearly equal sized parts the sample set should be divided into (outer cross-validation)
`cross.repeat`	integer - the number of repetitions of the cross-validation procedure
`cross.inner`	integer - the number of nearly equal sized parts the train set should be divided into (inner cross-validation)
`plot.label`	name of one column of the phenodata- if specified, the content of this column will form the labels of the x-axis if the 'votematrix' will be plotted with plot.MCRestimate
`rand`	integer - the random number generator will be put in a reproducible state
`stratify`	should a stratified version be used for the cross validation?
`block.column`	a character string which indicates the column of the expression set's phenodata containing the blocking covariate, which sets a constrain on the cross-validation splits: each block is either completely assigned to the test or to the training set
`information`	information - should classifier specific data be given(depends on the wrapper for the classification method)

The argument 'thePreprocessingMethods' can be used instead of 'variableSel.fun' and 'cluster.fun'. In the first versions of MCRestimate it was only possible to have one variable selection and one cluster functions. Now it is possible to have more than two functions and the ordering is arbitrary, e.g. you can have a variable selection function, then a cluster function and then a second variable selection function.

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.

an object of class MCRestimate which is a list with fourteen arguments:

`votes`	a matrix consisting of the different votes for each sample
`classes`	the class of each sample
`table`	a 'confusion' table, shows the number of 'correct prediction' for each class
`correct.prediction`	a logical vector - indicates if 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.
`correct.class.vote`	vector that contains for every sample the vote for it's correct class
`parameter`	a list consisting of the estimated 'best' parameter for each cross-validation part
`class.method`	string which names the function used for the classification
`thePreprocessingMethods`	character string - name of the preprocessing functions that have been used
`cross.outer`	number of blocks for a the outer cross-validation
`cross.repeat`	number of outer cross-validation repetitions
`cross.inner`	number of blocks for a the inner cross-validation
`sample.names`	names of the sample
`information`	classifier specific data (if information is TRUE)

Markus Ruschhaupt mailto:m.ruschhaupt@dkfz.de, contributions from Andreas Buness and Patrick Warnat

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)