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]