AvgPlm: The AvgPlm class


Package: aroma.affymetrix
Class 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.

Fields and 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, objectSize, print, save, asThis


For a single unit group, the averaging PLM of K probes is:

y_{ik} = θ_i + \varepsilon_{ik}

where θ_i are the chip effects for arrays i=1,...,I. The \varepsilon_{ik} are zero-mean noise with equal variance.

Different flavors of model fitting

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{θ}_{i} = 1/K ∑_k y_{ik}


Use argument flavor="median" to fit the model robustly, i.e.

\hat{θ}_{i} = median_k y_{ik}


Missing values are always excluded.


Henrik Bengtsson

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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