Description Usage Arguments Details Value Author(s) Examples
View source: R/normalizeLoessPara.R
Parallelized loess normalization of arrays.
1 2 3 4 5 6 7 |
object |
An object of class AffyBatch
OR a |
phenoData |
An AnnotatedDataFrame object. |
cdfname |
Used to specify the name of an alternative cdf package. If set to |
type |
A string specifying how the normalization should be applied. |
subset |
a subset of the data to fit a loess to. |
epsilon |
a tolerance value (supposed to be a small value - used as a stopping criterium). |
maxit |
maximum number of iterations. |
log.it |
logical. If |
span |
parameter to be passed the function loess |
family.loess |
parameter to be passed the function loess. "gaussian" or "symmetric" are acceptable values for this parameter. |
cluster |
A cluster object obtained from the function makeCluster in the SNOW package.
For default |
verbose |
A logical value. If |
Parallelized loess normalization of arrays.
For the serial function and more details see the function normalize.AffyBatch.loess
.
For using this function a computer cluster using the SNOW package has to be started.
Starting the cluster with the command makeCluster
generates an cluster object in the affyPara environment (.affyParaInternalEnv) and
no cluster object in the global environment. The cluster object in the affyPara environment will be used as default cluster object,
therefore no more cluster object handling is required.
The makeXXXcluster
functions from the package SNOW can be used to create an cluster object in the global environment and
to use it for the preprocessing functions.
In the loess normalization the arrays will compared by pairs. Therefore at every node minimum two arrays have to be!
An AffyBatch of normalized objects.
Markus Schmidberger schmidb@ibe.med.uni-muenchen.de, Ulrich Mansmann mansmann@ibe.med.uni-muenchen.de
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## Not run:
library(affyPara)
if (require(affydata)) {
data(Dilution)
makeCluster(3)
AffyBatch <- normalizeAffyBatchLoessPara(Dilution, verbose=TRUE)
stopCluster()
}
## End(Not run)
|
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