GeneSelector (version 2.22.0)

GenerateBootMatrix: 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.

Value

BootMatrix

warning

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

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).

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)