Package: aroma.affymetrix
Class AvgPlm
Object
~~|
~~+--ParametersInterface
~~~~~~~|
~~~~~~~+--Model
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~~~~~~~~~~~~+--UnitModel
~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~+--MultiArrayUnitModel
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~~~~~~~~~~~~~~~~~~~~~~+--ProbeLevelModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~+--AvgPlm
Directly known subclasses:
AvgCnPlm, AvgSnpPlm
public abstract static class AvgPlm
extends ProbeLevelModel
This class represents a PLM where the probe intensities are averaged assuming identical probe affinities. For instance, one may assume that replicated probes with identical sequences have the same probe affinities, cf. the GenomeWideSNP_6 chip type.
AvgPlm(..., flavor=c("median", "mean"))
Arguments passed to ProbeLevelModel.
A character string specifying what model fitting algorithm
to be used. This makes it possible to get identical estimates as other
packages.
Methods:
No methods defined.
Methods inherited from ProbeLevelModel:
calculateResidualSet, calculateWeights, fit, getAsteriskTags, getCalculateResidualsFunction, getChipEffectSet, getProbeAffinityFile, getResidualSet, getRootPath, getWeightsSet
Methods inherited from MultiArrayUnitModel:
getListOfPriors, setListOfPriors, validate
Methods inherited from UnitModel:
findUnitsTodo, getAsteriskTags, getFitSingleCellUnitFunction, getParameters
Methods inherited from Model:
as.character, fit, getAlias, getAsteriskTags, getDataSet, getFullName, getName, getPath, getRootPath, getTags, setAlias, setTags
Methods inherited from ParametersInterface:
getParameterSets, getParameters, getParametersAsString
Methods inherited from Object:
$, $<-, [[, [[<-, as.character, attach, attachLocally, clearCache, clearLookupCache, clone, detach, equals, extend, finalize, getEnvironment, getFieldModifier, getFieldModifiers, getFields, getInstantiationTime, getStaticInstance, hasField, hashCode, ll, load, names, objectSize, print, save, asThis
For a single unit group, the averaging PLM of K probes is:
$$y_{ik} = \theta_i + \varepsilon_{ik}$$
where \(\theta_i\) are the chip effects for arrays \(i=1,...,I\). The \(\varepsilon_{ik}\) are zero-mean noise with equal variance.
The above model can be fitted in two ways, either robustly or
non-robustly.
Use argument flavor="mean" to fit the model non-robustly, i.e.
$$\hat{\theta}_{i} = 1/K \sum_k y_{ik}$$.
Use argument flavor="median" to fit the model robustly, i.e.
$$\hat{\theta}_{i} = median_k y_{ik}$$.
Missing values are always excluded.
Henrik Bengtsson