varSel.highest.var.eSRG {MCRestimate} | R Documentation |
Different functions for a variable selection and clustering methods. These functions are used for the function MCRestimate.exprSetRG
varSel.highest.t.stat.eSRG(sample.gene.matrix,classfactor,theParameter=NULL,var.numbers=500,...) varSel.highest.var.eSRG(sample.gene.matrix,classfactor,theParameter=NULL,var.numbers=2000,...) varSel.green.int.max.eSRG(sample.gene.matrix,classfactor,theParameter=NULL,lambda=0.5,...) varSel.green.int.sec.eSRG(sample.gene.matrix,classfactor,theParameter=NULL, lambda=0.5,...)
sample.gene.matrix |
a matrix in which the rows corresponds to genes and the colums corresponds to samples |
classfactor |
a factor containing the values that should be predicted |
theParameter |
Either NULL or a logical vector which indicates for every gene if it should be left out from further analysis or not |
var.numbers |
some methods needs an argument which specifies how many variables should be taken |
lambda |
additional parameter for some methods |
... |
Further parameters |
varSel.highest.var.eSRG
selects a number (specified
by 'var.numbers') of variables with the highest variance.
varSel.highest.t.stat.eSRG
selects variables with highest t
statistics. varSel.green.int.max.eSRG
and
varSel.green.int.sec.eSRG
perform a selection based on the
green intensity channel. All functions only work with MCRestimate.exprSetRG
.
Every function returns a list consisting of two arguments:
matrix |
the result matrix of the variable redution or the clustering |
parameter |
The parameter which are used to reproduce the algorithm, i.e. a vector which indicates for every gene if it will be left out from further analysis or not if a gene reduction is performed or the output of the function kmeans for the clustering algorithm. |
Markus Ruschhaupt mailto:m.ruschhaupt@dkfz.de
library(MCRestimate) m <- matrix(c(rnorm(10,2,0.5),rnorm(10,4,0.5),rnorm(10,7,0.5),rnorm(10,2,0.5),rnorm(10,4,0.5),rnorm(10,2,0.5)),ncol=2) cluster.kmeans.mean(m ,number.clusters=3)