GenerateBootMatrix {GeneSelector} | R Documentation

## Altered datasets via bootstrap

### Description

Generates an object of class BootMatrix that is then processed by GetRepeatRanking

### Usage

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

### Arguments

`x` | A `matrix` of gene expression values with rows corresponding to genes and columns corresponding to observations. Can alternatively an object of class `ExpressionSet`. If `type = paired`, the first half of the columns corresponds to the first measurements and the second half to the second ones. For instance, if there are 10 observations, each measured twice, stored in an expression matrix `expr`, then `expr[,1]` is paired with `expr[,11]`, `expr[,2]` with `expr[,12]`, and so on.
`y` | If `x` is a matrix, then `y` may be a `numeric` vector or a factor with at most two levels. If `x` is an `ExpressionSet`, then `y` is a character specifyig the phenotype variable in the output from `pData`. If `type = paired`, take care that the coding is analogously to the requirement concerning `x`
`replicates` | Number of bootstrap replicates to be generated. Should rarely exceed 50.
`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 in a 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 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 contstraints 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 martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix

### References

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

### See Also

GenerateFoldMatrix, GetRepeatRanking

### Examples

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

[Package GeneSelector version 1.2.0 Index]