Object
~~|
~~+--ParametersInterface
~~~~~~~|
~~~~~~~+--Model
~~~~~~~~~~~~|
~~~~~~~~~~~~+--UnitModel
~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~+--MultiArrayUnitModel
~~~~~~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~~~~~~+--ProbeLevelModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~~~~~~~~~~~+--RmaPlm
Directly known subclasses:
ExonRmaPlm, HetLogAddCnPlm, HetLogAddPlm, HetLogAddSnpPlm, RmaCnPlm, RmaSnpPlm
public abstract static class RmaPlm
extends ProbeLevelModel
This class represents the log-additive model part of the Robust Multichip
Analysis (RMA) method described in Irizarry et al (2003).RmaPlm(..., flavor=c("affyPLM", "oligo"))ProbeLevelModel.character string specifying what model fitting algorithm
to be used. This makes it possible to get identical estimates as other
packages.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, getParameterSet, 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, gc, getEnvironment, getFieldModifier, getFieldModifiers, getFields, getInstantiationTime, getStaticInstance, hasField, hashCode, ll, load, objectSize, print, save, asThis
$$log_2(y_{ik}) = \beta_i + \alpha_k + \varepsilon_{ik}$$
where $\beta_i$ are the chip effects for arrays $i=1,...,I$, and $\alpha_k$ are the probe affinities for probes $k=1,...,K$. The $\varepsilon_{ik}$ are zero-mean noise with equal variance. The model is constrained such that $\sum_k{\alpha_k} = 0$.
Note that all PLM classes must return parameters on the intensity scale. For this class that means that $\theta_i = 2^\beta_i$ and $\phi_k = 2^\alpha_k$ are returned.
flavor="affyPLM") uses the implementation in the
Alternatively, other model-fitting algorithms are available.
The algorithm (flavor="oligo") used by the