GenerateBootMatrix {GeneSelector} | R Documentation

## Altered datasets via bootstrap

### Description

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

### Usage

    GenerateBootMatrix(x, y, replicates = 50, type = c("unpaired", "paired", "onesample"), maxties = NULL, minclassize = 2, balancedclass = FALSE, balancedsample = FALSE, control)

### Arguments

`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.

### Details

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).

### Value

An object of class `BootMatrix`

### warning

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

### Note

No bootstrap sample will occur more than once, i.e. each replication is unique.

### Author(s)

Martin Slawski
Anne-Laure Boulesteix

### References

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

### See Also

GenerateFoldMatrix, RepeatRanking

### Examples

    ## 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)

[Package GeneSelector version 2.30.0 Index]