GenerateBootMatrix {GeneSelector} | R Documentation |
Generates an object of class BootMatrix that is then processed by GetRepeatRanking
GenerateBootMatrix(x, y, replicates = 50, type = c("unpaired", "paired", "onesample"), maxties = NULL, minclassize = 2, balancedclass = FALSE, balancedsample = FALSE, control)
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. |
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).
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 martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
Davison, A.C., Hinkley, D.V. (1997)
Bootstrap Methods and their Application.
Cambridge University Press
GenerateFoldMatrix, GetRepeatRanking
## 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)