AffinePlm {aroma.affymetrix} | R Documentation |
Package: aroma.affymetrix
Class AffinePlm
Object
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~~+--
Model
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UnitModel
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MultiArrayUnitModel
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~~~~~~~~~~~~~~~~~+--
ProbeLevelModel
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AffinePlm
Directly known subclasses:
AffineCnPlm, AffineSnpPlm
public static class AffinePlm
extends ProbeLevelModel
This class represents affine model in Bengtsson & Hössjer (2006).
AffinePlm(..., background=TRUE)
... |
Arguments passed to ProbeLevelModel . |
background |
If TRUE , background is estimate for each unit group,
otherwise not. That is, if FALSE , a linear (proportional)
model without offset is fitted, resulting in very similar results as
obtained by the MbeiPlm . |
Methods:
getAsteriskTags | - | |
getProbeAffinityFile | - |
Methods inherited from ProbeLevelModel:
calculateResidualSet, calculateWeights, fit, getAsteriskTags, getCalculateResidualsFunction, getChipEffectSet, getProbeAffinityFile, getResidualSet, getWeightsSet
Methods inherited from MultiArrayUnitModel:
getListOfPriors, setListOfPriors, validate
Methods inherited from UnitModel:
findUnitsTodo, getAsteriskTags, getFitSingleCellUnitFunction
Methods inherited from Model:
fit, getAlias, getAsteriskTags, getDataSet, getFullName, getName, getPath, getRootPath, getTags, setAlias, setTags
Methods inherited from Object:
asThis, $, $<-, [[, [[<-, as.character, attach, attachLocally, clearCache, clone, detach, equals, extend, finalize, gc, getEnvironment, getFields, getInstantiationTime, getStaticInstance, hasField, hashCode, ll, load, objectSize, print, registerFinalizer, save
For a single unit group, the affine model is:
y_{ik} = a + theta_i phi_k + varepsilon_{ik}
where a is an offset common to all probe signals, theta_i are the chip effects for arrays i=1,...,I, and phi_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 prod_k phi_k = 1.
Note that with the additional constraint a=0 (see arguments above),
the above model is very similar to MbeiPlm
. The differences in
parameter estimates is due to difference is assumptions about the
error structure, which in turn affects how the model is estimated.
Henrik Bengtsson (http://www.braju.com/R/)
Bengtsson & Hössjer (2006).