GenerateFoldMatrix {GeneSelector} | R Documentation |
Generates an object of class FoldMatrix that is then processed by GetRepeatRanking
GenerateFoldMatrix(x, y, k = 1, replicates = ifelse(k==1, ncol(x), 10), type = c("unpaired", "paired", "onesample"), minclassize = 2, balanced = 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 |
k |
Number of observations that are removed or whose labels are exchanged. Label exchange means that the actual label is replaced by the label of the other class (s. GetRepeatRanking). |
replicates |
Number of replications if k>1 . |
type |
One of "paired", "unpaired", "onesample" , depends
on the type of test to be performed, s. for example
RankingTstat. |
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 replication. |
balanced |
If balanced=TRUE , then the proportions
of the two classes are (at least approximately) the same
for each replication. It is a shortcut for a certain value of
minclasssize . May not reasonable, if class
proportions are unbalanced. |
control |
Further control arguments concerning the generation process of the fold matrix, s. samplingcontrol. |
An object of class FoldMatrix.
If the generation process (partially) fails, try to
reduce the constraints or change the argument control
.
No jackknif-ed dataset 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
GenerateBootMatrix, GetRepeatRanking
## Load toy gene expression data data(toydata) ### class labels yy <- toydata[1,] ### gene expression xx <- toydata[-1,] ### Generate Leave-One-Out / Exchange-One-Label matrix loo <- GenerateFoldMatrix(xx, yy, k=1) ### A more complex example l3o <- GenerateFoldMatrix(xx, yy, k=3, replicates=30, minclassize=5)