GenerateBootMatrix: Altered datasets via bootstrap
In GeneSelector: Stability and Aggregation of ranked gene lists

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

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

1	GenerateBootMatrix(x, y, replicates = 50, type = c("unpaired", "paired", "onesample"), maxties = NULL, minclassize = 2, balancedclass = FALSE, balancedsample = 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 `pData`. If `type = "paired"`, take care that the coding is correct.
`replicates`	Number of bootstrap replicates to be generated.
`type`	One of `"paired", "unpaired", "onesample"`, depends on the type of test to be performed, s. for example RankingTstat.
`maxties`	The maximum number of ties allowed per observation. For example, `maxties=2` means that no observation occurs more than `maxties+1 = 3` times per bootstrap sample.
`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 bootstrap sample.
`balancedclass`	If `balancedclass=TRUE`, then the proportions of the two classes are the same for each bootstrap sample. It is a shortcut for a certain value of `minclasssize`. May not be reasonable if class proportions are unbalanced in the original sample.
`balancedsample`	Should balanced bootstrap (s. details) be performed ?
`control`	Further control arguments concerning the generation process of the bootstrap matrix, s. samplingcontrol.

For the case that balancedsample=TRUE, all other constraints as imposed by maxties, minclassize and so on are ignored. Balanced bootstrap (s. reference below) means that each observation occurs equally frequently (with respect to all bootstrap replications).

An object of class BootMatrix

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

No bootstrap sample 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

GenerateFoldMatrix, RepeatRanking

## Load toy gene expression data
data(toydata)
### class labels
yy <- toydata[1,]
### Generate Boot Matrix, maximum number of ties=3,
### minimum classize=5, 30 replications:
boot <- GenerateBootMatrix(y = yy, maxties=3, minclassize=5, repl=30)