GenerateFoldMatrix: Altered datasets via k-Jackknife or label exchange
In GeneSelector: Stability and Aggregation of ranked gene lists

Description Usage Arguments Value warning Note Author(s) References See Also Examples

Generates an object of class FoldMatrix to be used for RepeatRanking.

1	GenerateFoldMatrix(x, y, k = 1, replicates = ifelse(k==1, length(y), 10), type = c("unpaired", "paired", "onesample"), minclassize = 2, balanced = FALSE, control)

`x`	Only needed if `y` is stored within an `ExpressionSet`.
`y`	`y` may be a `numeric` vector or a factor with at most two levels. If `x` is an `ExpressionSet`, then `y` is a character specifying the phenotype variable in the output from `pData`. If `type = "paired"`, take care that the coding is correct.
`k`	Number of observations that are removed or whose labels are exchanged. Label exchange means that the observed label is replaced by the label of the other class (s. RepeatRanking).
`replicates`	Number of replications if `k>1`.
`type`	One of `"paired", "unpaired", "onesample"`, depends on the type of test to be performed, s. for example RankingTstat.
`minclassize`	If `minclassize=k` for some integer `k`, then the number of observations in each class are grater then or equal to `minclassize` for each replication.
`balanced`	If `balanced=TRUE`, then the proportions of the two classes are (at least approximately) the same for each replication. It is a shortcut for a certain value of `minclasssize`. May not be reasonable if class proportions in the given dataset are unbalanced in the original sample.
`control`	Further control arguments concerning the generation process of the fold matrix, s. samplingcontrol.

An object of class FoldMatrix.

If the generation process (partially) fails, try to reduce the constraints or change the argument control.

No jackknif-ed dataset will occur more than once, i.e. each replication is unique.

Martin Slawski
Anne-Laure Boulesteix

Davison, A.C., Hinkley, D.V. (1997)
Bootstrap Methods and their Application. Cambridge University Press

GenerateBootMatrix, RepeatRanking

 ## Load toy gene expression data
data(toydata)
### class labels
yy <- toydata[1,]
### Generate Leave-One-Out / Exchange-One-Label matrix
loo <- GenerateFoldMatrix(y = yy, k=1)
### A more complex example
l3o <- GenerateFoldMatrix(y = yy, k=3, replicates=30, minclassize=5)